Methods and compositions for detecting and modulating an immunotherapy resistance gene signature in cancer

ABSTRACT

The subject matter disclosed herein is generally directed to detecting and modulating novel gene signatures for the treatment and prognosis of cancer. The novel gene signatures predict overall survival in cancer and can be targeted therapeutically.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application Nos.62/480,407, filed Apr. 1, 2017, 62/519,784, filed Jun. 14, 2017,62/567,153, filed Oct. 2, 2017, 62/573,117, filed Oct. 16, 2017,62/588,025, filed Nov. 17, 2017, 62/595,327, filed Dec. 6, 2017 and62/630,158, filed Feb. 13, 2018. The entire contents of theabove-identified applications are hereby fully incorporated herein byreference.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

This invention was made with government support under grant Nos.CA222663, CA180922, CA202820 and CA14051 awarded by the NationalInstitutes of Health. The government has certain rights in theinvention.

TECHNICAL FIELD

The subject matter disclosed herein is generally directed to detectingand modulating novel gene signatures for the treatment and prognosis ofcancer.

BACKGROUND

One reason that cancer cells thrive is because they are able to hidefrom the immune system. Certain cancer cells avoid the immune systembetter than others and could be a factor in determining survival.Immunotherapies have been developed to enhance immune responses againstcancer and lead to prolonged survival. Immunotherapies have transformedthe therapeutic landscape of several cancer types. In particular, immunecheckpoint inhibitors (ICI) lead to durable responses in ˜35% ofpatients with metastatic melanoma by unleashing T cells from oncogenicsuppression (1, 2). Nonetheless, the tumors of most melanoma patientsmanifest either intrinsic or acquired ICI resistance (ICR). ICR is oftenunpredictable and poorly understood (3), hampering appropriate selectionof patients for therapies, rational enrollment to clinical trials andthe development of new therapeutic strategies that could overcome ICR(1).

Recent clinical studies attempted to characterize and predict ICR basedon analyses of Whole Exome Sequencing (WES) and transcriptional profilesof tumors at the bulk level (4, 5). These studies demonstrated thattumors with a high mutational load (4) or high immune cell infiltration(6, 7) are more likely to respond, and linked ICR in patients tofunctional immune evasion phenotypes, including defects in the JAK/STATpathway (8) and interferon gamma (IFN-γ) response (8, 9), impairedantigen presentation (5, 8), PTEN loss (10), and increased WNT-β-cateninsignaling (11). However, thus far, the predictive power of these andother (12) approaches has been limited, either because they report ononly some facets of the causes of resistance (WES) and/or because theyare highly confounded by tumor composition (RNA and copy-numbervariations). Indeed, because ICI targets the interactions betweendifferent cells in the tumor, its impact depends on multicellularcircuits of malignant and non-malignant cells (13), which arechallenging to study in bulk tumor specimens. Single-cell genomics,especially single cell RNA-Seq (scRNA-Seq), provides a unique tool tocomprehensively map the tumor ecosystem (13-17), but has thus far notbeen used to study ICR. Thus, there is a need to better understand tumorimmunity and resistance to immunotherapy.

Citation or identification of any document in this application is not anadmission that such document is available as prior art to the presentinvention.

SUMMARY

Immune checkpoint inhibitors (ICI) produce durable responses in somepatients with melanoma. Yet most patients derive no clinical benefit,and molecular underpinnings of ICI resistance (ICR) are elusive.

It is an objective of the present invention to identify molecularsignatures for diagnosis, prognosis and treatment of subjects sufferingfrom cancer. It is a further objective to understand tumor immunity andto leverage this knowledge for treating subjects suffering from cancer.It is another objective for identifying gene signatures for predictingresponse to checkpoint blockade therapy. It is another objective, formodulating the molecular signatures in order to increase efficacy ofimmunotherapy (e.g., checkpoint blockade therapy).

Here, Applicants leveraged single-cell RNA-seq (scRNA-seq) from 31melanoma tumors and novel computational methods to systematicallyinterrogate malignant cell states that promote immune evasion.Applicants identified a resistance program expressed by malignant cellsthat is strongly associated with T cell exclusion and direct evasionfrom immunity. The program is present prior to immunotherapy, isapparent in situ, and predicts clinical responses to anti-PD-1 therapyin an independent cohort of 112 melanoma patients. CDK4/6-inhibitionrepresses this program in individual malignant cells and induces aSenescence Associated Secretory Phenotype (SASP). This study provides ahigh-resolution landscape of ICI resistant cell states, identifiesclinically predictive signatures, and forms a basis to develop noveltherapeutic strategies that could overcome immunotherapy resistance.Applicants additionally applied single-nuclei RNA-seq (sNuc-seq) tocharacterize thousands of cells from estrogen-receptor-positivemetastatic breast cancer (MBC). ER+ MBC is currently treated withCDK4/6-inhibitors (see, e.g., Vasan et al., State-of-the-Art Update:CDK4/6 Inhibitors in ER+ Metastatic Breast Cancer, AJHO. 2017;13(4):16-22). Finally, Applicants applied single-cell RNA-seq(scRNA-seq) to characterize thousands of cells from colon cancer.

In one aspect, the present invention provides for a method of detectingan immune checkpoint inhibitor resistance (ICR) gene signature in atumor comprising, detecting in tumor cells obtained from a subject inneed thereof the expression or activity of a malignant cell genesignature comprising: one or more genes or polypeptides selected fromthe group consisting of C1QBP, CCT2, CCT6A, DCAF13, EIF4A1, ILF2,MAGEA4, NONO, PA2G4, PGAM1, PPA1, PPIA, RPL18A, RPL26, RPL31, RPS11,RPS15, RPS21, RPS5, RUVBL2, SAE1, SNRPE, UBA52, UQCRH, VDAC2, AEBP1,AHNAK, APOC2, APOD, APOE, B2M, C10orf54, CD63, CTSD, EEA1, EMP1, FBXO32,FYB, GATSL3, HCP5, HLA-A, HLA-B, HLA-C, HLA-E, HLA-F, HLA-H, ITGA3,LAMP2, LYRM9, MFGE8, MIA, NPC2, NSG1, PROS1, RDH5, SERPINA1, TAPBP,TIMP2, TNFSF4 and TRIML2 (refined uICR, see table S6); or one or moregenes or polypeptides selected from the group consisting of ACAT1, ACP5,ACTB, ACTG1, ADSL, AEN, AK2, ANP32E, APP, ASAP1, ATP5A1, ATP5D, ATP5G2,BANCR, BCAN, BZW2, C17orf76-AS1, C1QBP, C20orf112, C6orf48, CA14, CBX5,CCT2, CCT3, CCT6A, CDK4, CEP170, CFL1, CHP1, CNRIP1, CRABP2, CS, CTPS1,CYC1, DAP3, DCAF13, DCT, DDX21, DDX39B, DLL3, EDNRB, EEF1D, EEF1G, EEF2,EIF1AX, EIF2S3, EIF3E, EIF3K, EIF3L, EIF4A1, EIF4EBP2, ESRP1, FAM174B,FAM178B, FAM92A1, FBL, FBLN1, FOXRED2, FTL, FUS, GABARAP, GAS5, GNB2L1,GPATCH4, GPI, GRWD1, GSTO1, H3F3A, H3F3AP4, HMGA1, HNRNPA1, HNRNPA1P10,HNRNPC, HSPA8, IDH2, IFI16, ILF2, IMPDH2, ISYNA1, ITM2C, KIAA0101,LHFPL3-AS1, LOC100190986, LYPLA1, MAGEA4, MARCKS, MDH2, METAP2, MID1,MIR4461, MLLT11, MPZL1, MRPL37, MRPS12, MRPS21, MYC, NACA, NCL, NDUFS2,NF2, NID1, NOLC1, NONO, NPM1, NUCKS1, OAT, PA2G4, PABPC1, PAFAH1B3,PAICS, PFDN2, PFN1, PGAM1, PIH1D1, PLTP, PPA1, PPIA, PPP2R1A, PSAT1,PSMD4, PTMA, PYCARD, RAN, RASA3, RBM34, RNF2, RPAIN, RPL10, RPL10A,RPL11, RPL12, RPL13, RPL13A, RPL13AP5, RPL14, RPL17, RPL18, RPL18A,RPL21, RPL26, RPL28, RPL29, RPL3, RPL30, RPL31, RPL35, RPL36A, RPL37,RPL37A, RPL39, RPL4, RPL41, RPL5, RPL6, RPL7, RPL7A, RPL8, RPLP0, RPLP1,RPS10, RPS11, RPS12, RPS15, RPS15A, RPS16, RPS17, RPS17L, RPS18, RPS19,RPS2, RPS21, RPS23, RPS24, RPS26, RPS27, RPS27A, RPS3, RPS3A, RPS4X,RPS5, RPS6, RPS7, RPS8, RPS9, RPSA, RSL1D1, RUVBL2, SAE1, SCD, SCNM1,SERBP1, SERPINF1, SET, SF3B4, SHMT2, SKP2, SLC19A1, SLC25A3, SLC25A5,SLC25A6, SMS, SNAI2, SNHG16, SNHG6, SNRPE, SORD, SOX4, SRP14, SSR2,TIMM13, TIMM50, TMC6, TOP1MT, TP53, TRAP1, TRPM1, TSR1, TUBA1B, TUBB,TUBB4A, TULP4, TXLNA, TYRP1, UBA52, UCK2, UQCRFS1, UQCRH, USP22, VCY1B,VDAC2, VPS72, YWHAE, ZFAS1, ZNF286A, A2M, ACSL3, ACSL4, ADM, AEBP1, AGA,AHNAK, ANGPTL4, ANXA1, ANXA2, APLP2, APOC2, APOD, APOE, ARF5, ARL6IP5,ATF3, ATP1A1, ATP1B1, ATP1B3, ATRAID, B2M, BACE2, BBX, BCL6, C10orf54,C4A, CALU, CASP1, CAST, CAV1, CBLB, CCND3, CD151, CD44, CD47, CD58,CD59, CD63, CD9, CDH19, CHI3L1, CHN1, CLIC4, CLU, CPVL, CRELD1, CRYAB,CSGALNACT1, CSPG4, CST3, CTSA, CTSB, CTSD, CTSL1, DAG1, DCBLD2, DDR1,DDX5, DPYSL2, DSCR8, DUSP4, DUSP6, DYNLRB1, ECM1, EEA1, EGR1, EMP1,EPHX2, ERBB3, EVA1A, EZH1, EZR, FAM3C, FBXO32, FCGR2C, FCRLA, FGFR1,FLJ43663, FOS, FYB, GAA, GADD45B, GATSL3, GEM, GOLGB1, GPNMB, GRN, GSN,HCP5, HLA-A, HLA-B, HLA-C, HLA-E, HLA-F, HLA-H, HPCAL1, HSPA1A, HSPA1B,HTATIP2, ID2, IFI27L2, IFI35, IGF1R, IL1RAP, IL6ST, ISCU, ITGA3, ITGA6,ITGA7, ITGB1, ITGB3, ITM2B, JUN, KCNN4, KLF4, KLF6, KRT10, LAMP2,LEPROT, LGALS1, LGALS3, LGALS3BP, LOC100506190, LPL, LRPAP1, LTBP3,LYRM9, MAEL, MAGEC2, MAP1B, MATN2, MFGE8, MFI2, MIA, MRPS6, MT1E, MT1M,MT1X, MT2A, NDRG1, NEAT1, NFKBIA, NFKBIZ, NNMT, NPC1, NPC2, NR4A1, NSG1,OCIAD2, PAGES, PDK4, PERP, PKM, PLP2, PRKCDBP, PRNP, PROS1, PRSS23,PSAP, PSMB9, PTRF, RDH5, RNF145, RPS4Y1, S100A13, S100A6, S100B, SAT1,SCARB2, SCCPDH, SDC3, SEL1L, SEMA3B, SERPINA1, SERPINA3, SERPINE2, SGCE,SGK1, SLC20A1, SLC26A2, SLC39A14, SLC5A3, SNX9, SOD1, SPON2, SPRY2,SQSTM1, SRPX, STOM, SYNGR2, SYPL1, TAPBP, TAPBPL, TF, TGOLN2, THBD,TIMP1, TIMP2, TIMP3, TIPARP, TM4SF1, TMBIM6, TMED10, TMED9, TMEM66,TMX4, TNC, TNFSF4, TPP1, TRIML2, TSC22D3, TSPYL2, TXNIP, TYR, UBC, UPP1,XAGE1A, XAGE1B, XAGE1C, XAGE1D, XAGE1E, ZBTB20 and ZBTB38 (uICR, seetable S6); or one or more genes or polypeptides selected from the groupconsisting of ANP32E, CTPS1, DDX39B, EIF4A1, ESRP1, FBL, FUS, HNRNPA1,ILF2, KIAA0101, NUCKS1, PTMA, RPL21, RUVBL2, SET, SLC25A5, TP53, TUBA1B,UCK2, YWHAE, APLP2, ARL6IP5, CD63, CLU, CRELD1, CTSD, CTSL1, FOS, GAA,GRN, HLA-F, ITM2B, LAMP2, MAP1B, NPC2, PSAP, SCARB2, SDC3, SEL1L, TMED10and TSC22D3 (uICR, see FIG. 3C); or one or more genes or polypeptidesselected from the group consisting of MT1E, MT1M, MT1X and MT2A.

In certain embodiments, the ICR signature may comprises a ICR-downsignature, said signature comprising one or more genes selected from thegroup consisting of: AEBP1, AHNAK, APOC2, APOD, APOE, B2M, C10orf54,CD63, CTSD, EEA1, EMP1, FBXO32, FYB, GATSL3, HCP5, HLA-A, HLA-B, HLA-C,HLA-E, HLA-F, HLA-H, ITGA3, LAMP2, LYRM9, MFGE8, MIA, NPC2, NSG1, PROS1,RDH5, SERPINA1, TAPBP, TIMP2, TNFSF4 and TRIML2 (refined uICR-down, seetable S6); or A2M, ACSL3, ACSL4, ADM, AEBP1, AGA, AHNAK, ANGPTL4, ANXA1,ANXA2, APLP2, APOC2, APOD, APOE, ARF5, ARL6IP5, ATF3, ATP1A1, ATP1B1,ATP1B3, ATRAID, B2M, BACE2, BBX, BCL6, C10orf54, C4A, CALU, CASP1, CAST,CAV1, CBLB, CCND3, CD151, CD44, CD47, CD58, CD59, CD63, CD9, CDH19,CHI3L1, CHN1, CLIC4, CLU, CPVL, CRELD1, CRYAB, CSGALNACT1, CSPG4, CST3,CTSA, CTSB, CTSD, CTSL1, DAG1, DCBLD2, DDR1, DDX5, DPYSL2, DSCR8, DUSP4,DUSP6, DYNLRB1, ECM1, EEA1, EGR1, EMP1, EPHX2, ERBB3, EVA1A, EZH1, EZR,FAM3C, FBXO32, FCGR2C, FCRLA, FGFR1, FLJ43663, FOS, FYB, GAA, GADD45B,GATSL3, GEM, GOLGB1, GPNMB, GRN, GSN, HCP5, HLA-A, HLA-B, HLA-C, HLA-E,HLA-F, HLA-H, HPCAL1, HSPA1A, HSPA1B, HTATIP2, ID2, IFI27L2, IFI35,IGF1R, IL1RAP, IL6ST, ISCU, ITGA3, ITGA6, ITGA7, ITGB1, ITGB3, ITM2B,JUN, KCNN4, KLF4, KLF6, KRT10, LAMP2, LEPROT, LGALS1, LGALS3, LGALS3BP,LOC100506190, LPL, LRPAP1, LTBP3, LYRM9, MAEL, MAGEC2, MAP1B, MATN2,MFGE8, MFI2, MIA, MRPS6, MT1E, MT1M, MT1X, MT2A, NDRG1, NEAT1, NFKBIA,NFKBIZ, NNMT, NPC1, NPC2, NR4A1, NSG1, OCIAD2, PAGES, PDK4, PERP, PKM,PLP2, PRKCDBP, PRNP, PROS1, PRSS23, PSAP, PSMB9, PTRF, RDH5, RNF145,RPS4Y1, S100A13, S100A6, S100B, SAT1, SCARB2, SCCPDH, SDC3, SEL1L,SEMA3B, SERPINA1, SERPINA3, SERPINE2, SGCE, SGK1, SLC20A1, SLC26A2,SLC39A14, SLC5A3, SNX9, SOD1, SPON2, SPRY2, SQSTM1, SRPX, STOM, SYNGR2,SYPL1, TAPBP, TAPBPL, TF, TGOLN2, THBD, TIMP1, TIMP2, TIMP3, TIPARP,TM4SF1, TMBIM6, TMED10, TMED9, TMEM66, TMX4, TNC, TNFSF4, TPP1, TRIML2,TSC22D3, TSPYL2, TXNIP, TYR, UBC, UPP1, XAGE1A, XAGE1B, XAGE1C, XAGE1D,XAGE1E, ZBTB20 and ZBTB38 (uICR-down, see table S6); or APLP2, ARL6IP5,CD63, CLU, CRELD1, CTSD, CTSL1, FOS, GAA, GRN, HLA-F, ITM2B, LAMP2,MAP1B, NPC2, PSAP, SCARB2, SDC3, SEL1L, TMED10 and TSC22D3 (uICR-down,see FIG. 3C), wherein said ICR-down signature is downregulated in atumor with a high ICR score and upregulated in a tumor with a low ICRscore.

In certain embodiments, the ICR signature comprises a ICR-up signature,said signature comprising one or more genes selected from the groupconsisting of: C1QBP, CCT2, CCT6A, DCAF13, EIF4A1, ILF2, MAGEA4, NONO,PA2G4, PGAM1, PPA1, PPIA, RPL18A, RPL26, RPL31, RPS11, RPS15, RPS21,RPS5, RUVBL2, SAE1, SNRPE, UBA52, UQCRH and VDAC2 (refined uICR-up, seetable S6); or ACAT1, ACP5, ACTB, ACTG1, ADSL, AEN, AK2, ANP32E, APP,ASAP1, ATP5A1, ATP5D, ATP5G2, BANCR, BCAN, BZW2, C17orf76-AS1, C1QBP,C20orf112, C6orf48, CA14, CBX5, CCT2, CCT3, CCT6A, CDK4, CEP170, CFL1,CHP1, CNRIP1, CRABP2, CS, CTPS1, CYC1, DAP3, DCAF13, DCT, DDX21, DDX39B,DLL3, EDNRB, EEF1D, EEF1G, EEF2, EIF1AX, EIF2S3, EIF3E, EIF3K, EIF3L,EIF4A1, EIF4EBP2, ESRP1, FAM174B, FAM178B, FAM92A1, FBL, FBLN1, FOXRED2,FTL, FUS, GABARAP, GAS5, GNB2L1, GPATCH4, GPI, GRWD1, GSTO1, H3F3A,H3F3AP4, HMGA1, HNRNPA1, HNRNPA1P10, HNRNPC, HSPA8, IDH2, IF116, ILF2,IMPDH2, ISYNA1, ITM2C, KIAA0101, LHFPL3-AS1, LOC100190986, LYPLA1,MAGEA4, MARCKS, MDH2, METAP2, MID1, MIR4461, MLLT11, MPZL1, MRPL37,MRPS12, MRPS21, MYC, NACA, NCL, NDUFS2, NF2, NID1, NOLC1, NONO, NPM1,NUCKS1, OAT, PA2G4, PABPC1, PAFAH1B3, PAICS, PFDN2, PFN1, PGAM1, PIH1D1,PLTP, PPA1, PPIA, PPP2R1A, PSAT1, PSMD4, PTMA, PYCARD, RAN, RASA3,RBM34, RNF2, RPAIN, RPL10, RPL10A, RPL11, RPL12, RPL13, RPL13A,RPL13AP5, RPL14, RPL17, RPL18, RPL18A, RPL21, RPL26, RPL28, RPL29, RPL3,RPL30, RPL31, RPL35, RPL36A, RPL37, RPL37A, RPL39, RPL4, RPL41, RPL5,RPL6, RPL7, RPL7A, RPL8, RPLP0, RPLP1, RPS10, RPS11, RPS12, RPS15,RPS15A, RPS16, RPS17, RPS17L, RPS18, RPS19, RPS2, RPS21, RPS23, RPS24,RPS26, RPS27, RPS27A, RPS3, RPS3A, RPS4X, RPS5, RPS6, RPS7, RPS8, RPS9,RPSA, RSL1D1, RUVBL2, SAE1, SCD, SCNM1, SERBP1, SERPINF1, SET, SF3B4,SHMT2, SKP2, SLC19A1, SLC25A3, SLC25A5, SLC25A6, SMS, SNAI2, SNHG16,SNHG6, SNRPE, SORD, SOX4, SRP14, SSR2, TIMM13, TIMM50, TMC6, TOP1MT,TP53, TRAP1, TRPM1, TSR1, TUBA1B, TUBB, TUBB4A, TULP4, TXLNA, TYRP1,UBA52, UCK2, UQCRFS1, UQCRH, USP22, VCY1B, VDAC2, VPS72, YWHAE, ZFAS1and ZNF286A (uICR-up, see table S6); or ANP32E, CTPS1, DDX39B, EIF4A1,ESRP1, FBL, FUS, HNRNPA1, ILF2, KIAA0101, NUCKS1, PTMA, RPL21, RUVBL2,SET, SLC25A5, TP53, TUBA1B, UCK2 and YWHAE (uICR-up, see FIG. 3C),wherein said ICR-up signature is upregulated in a tumor with a high ICRscore and downregulated in a tumor with a low ICR score.

In another aspect, the present invention provides for a method ofdetecting an immune checkpoint inhibitor resistance (ICR) gene signaturein a tumor comprising, detecting in tumor cells obtained from a subjectin need thereof the expression or activity of a malignant cell genesignature comprising: one or more genes or polypeptides selected fromthe group consisting of ACTB, AEN, ANP32E, ATP5A1, ATP5G2, BZW2,C17orf76-AS1, C1QBP, C20orf112, CA14, CBX5, CCT2, CCT3, CDK4, CFL1,CNRIP1, CRABP2, CS, CTPS1, DCAF13, DCT, DDX39B, DLL3, EEF1G, EIF2S3,EIF3K, EIF4A1, EIF4EBP2, FAM174B, FBL, FBLN1, FOXRED2, FTL, FUS,GABARAP, GAS5, GNB2L1, GPATCH4, GPI, GRWD1, H3F3A, H3F3AP4, HMGA1,HNRNPA1, HNRNPA1P10, HNRNPC, HSPA8, IDH2, ILF2, ISYNA1, ITM2C, KIAA0101,MAGEA4, MDH2, METAP2, MID1, MIR4461, MLLT11, MPZL1, MRPS21, NACA, NCL,NDUFS2, NOLC1, NONO, PA2G4, PABPC1, PAFAH1B3, PFDN2, PFN1, PGAM1,PIH1D1, PPA1, PPIA, PPP2R1A, PSMD4, PTMA, RAN, RBM34, RNF2, RPAIN,RPL10A, RPL11, RPL12, RPL13, RPL13A, RPL13AP5, RPL17, RPL18, RPL18A,RPL21, RPL26, RPL28, RPL29, RPL3, RPL31, RPL36A, RPL37, RPL37A, RPL39,RPL4, RPL41, RPL5, RPL6, RPL8, RPLP0, RPLP1, RPS10, RPS11, RPS12,RPS15A, RPS16, RPS17, RPS17L, RPS18, RPS19, RPS21, RPS23, RPS24, RPS26,RPS27, RPS27A, RPS3, RPS4X, RPS5, RPS6, RPS7, RPS8, RPS9, RPSA, RUVBL2,SAE1, SCD, SCNM1, SERPINF1, SET, SF3B4, SHMT2, SKP2, SLC25A3, SMS,SNAI2, SNHG6, SNRPE, SOX4, SRP14, SSR2, TIMM50, TMC6, TP53, TRPM1, TSR1,TUBA1B, TUBB, TULP4, UBA52, UQCRFS1, UQCRH, USP22, VCY1B, VDAC2, VPS72,YWHAE, ZNF286A, A2M, ACSL3, ACSL4, ADM, AEBP1, AGA, AHNAK, ANGPTL4,ANXA1, ANXA2, APLP2, APOD, APOE, ARL6IP5, ATF3, ATP1A1, ATP1B1, ATP1B3,B2M, BACE2, BBX, BCL6, CALU, CASP1, CAST, CAV1, CCND3, CD151, CD44,CD47, CD58, CD59, CD63, CD9, CDH19, CHI3L1, CLIC4, CRELD1, CRYAB,CSGALNACT1, CSPG4, CST3, CTSA, CTSB, CTSD, CTSL1, DAG1, DCBLD2, DDR1,DDX5, DPYSL2, DUSP4, DUSP6, ECM1, EEA1, EGR1, EMP1, EPHX2, ERBB3, EVA1A,EZH1, FAM3C, FBXO32, FCGR2C, FCRLA, FGFR1, FLJ43663, FOS, GAA, GADD45B,GEM, GOLGB1, GPNMB, GRN, GSN, HLA-A, HLA-B, HLA-C, HLA-E, HLA-F, HLA-H,HPCAL1, HSPA1A, HTATIP2, IFI35, IGF1R, IL1RAP, IL6ST, ITGA3, ITGA6,ITGB1, ITGB3, ITM2B, JUN, KCNN4, KLF4, KLF6, LAMP2, LEPROT, LGALS1,LGALS3, LGALS3BP, LPL, LRPAP1, MAGEC2, MFGE8, MFI2, MIA, MT1E, MT1M,MT1X, MT2A, NEAT1, NFKBIA, NFKBIZ, NNMT, NPC1, NPC2, NR4A1, NSG1, PDK4,PLP2, PRKCDBP, PRNP, PROS1, PRSS23, PSAP, PSMB9, PTRF, RNF145, RPS4Y1,S100A6, S100B, SAT1, SCARB2, SCCPDH, SDC3, SEL1L, SEMA3B, SERPINA3,SERPINE2, SGCE, SGK1, SLC20A1, SLC26A2, SLC39A14, SLC5A3, SOD1, SPRY2,SQSTM1, SRPX, STOM, SYNGR2, SYPL1, TAPBP, TAPBPL, TF, TGOLN2, TIMP1,TIMP2, TIMP3, TIPARP, TM4SF1, TMED10, TMED9, TMEM66, TMX4, TNC, TPP1,TSC22D3, TYR, UBC, UPP1, ZBTB20 and ZBTB38 (oncogenic ICR, see tableS6); or one or more genes or polypeptides selected from the groupconsisting of AEN, ATP5A1, C20orf112, CCT2, DCAF13, DDX39B, ISYNA1,NDUFS2, NOLC1, PA2G4, PPP2R1A, RBM34, RNF2, RPL6, RPL21, SERPINF1,SF3B4, SMS, TMC6, VPS72, ANXA1, ATF3, BCL6, CD58, CD9, CTSB, DCBLD2,EMP1, HLA-F, HTATIP2, IL1RAP, ITGA6, KCNN4, KLF4, MT1E, MT1M, MT1X,MT2A, NNMT, PRKCDBP, S100A6 and TSC22D3 (oncogenic ICR, see FIG. 2B); orone or more genes or polypeptides selected from the group consisting ofACTB, ANP32E, CBX5, FUS, HNRNPA1, IDH2, KIAA0101, NCL, PFN1, PPIA, PTMA,RAN, RPLP0, TUBA1B, TUBB, VCY1B, A2M, APOD, BCL6, CD44, CD59, CD63,CDH19, CHI3L1, CTSA, CTSB, CTSD, FOS, GPNMB, GRN, HLA-A, HLA-B, HLA-H,ITM2B, LGALS3BP, NEAT1, PDK4, PSAP, SCARB2, SERPINA3, SLC26A2, TAPBPL,TMEM66 and TYR (oncogenic ICR, see FIG. 10B); or one or more genes orpolypeptides selected from the group consisting of MT1E, MT1M, MT1X andMT2A.

In certain embodiments, the ICR signature comprises an ICR-downsignature, said signature comprising one or more genes selected from thegroup consisting of: A2M, ACSL3, ACSL4, ADM, AEBP1, AGA, AHNAK, ANGPTL4,ANXA1, ANXA2, APLP2, APOD, APOE, ARL6IP5, ATF3, ATP1A1, ATP1B1, ATP1B3,B2M, BACE2, BBX, BCL6, CALU, CASP1, CAST, CAV1, CCND3, CD151, CD44,CD47, CD58, CD59, CD63, CD9, CDH19, CHI3L1, CLIC4, CRELD1, CRYAB,CSGALNACT1, CSPG4, CST3, CTSA, CTSB, CTSD, CTSL1, DAG1, DCBLD2, DDR1,DDX5, DPYSL2, DUSP4, DUSP6, ECM1, EEA1, EGR1, EMP1, EPHX2, ERBB3, EVA1A,EZH1, FAM3C, FBXO32, FCGR2C, FCRLA, FGFR1, FLJ43663, FOS, GAA, GADD45B,GEM, GOLGB1, GPNMB, GRN, GSN, HLA-A, HLA-B, HLA-C, HLA-E, HLA-F, HLA-H,HPCAL1, HSPA1A, HTATIP2, IFI35, IGF1R, IL1RAP, IL6ST, ITGA3, ITGA6,ITGB1, ITGB3, ITM2B, JUN, KCNN4, KLF4, KLF6, LAMP2, LEPROT, LGALS1,LGALS3, LGALS3BP, LPL, LRPAP1, MAGEC2, MFGE8, MFI2, MIA, MT1E, MT1M,MT1X, MT2A, NEAT1, NFKBIA, NFKBIZ, NNMT, NPC1, NPC2, NR4A1, NSG1, PDK4,PLP2, PRKCDBP, PRNP, PROS1, PRSS23, PSAP, PSMB9, PTRF, RNF145, RPS4Y1,S100A6, S100B, SAT1, SCARB2, SCCPDH, SDC3, SEL1L, SEMA3B, SERPINA3,SERPINE2, SGCE, SGK1, SLC20A1, SLC26A2, SLC39A14, SLC5A3, SOD1, SPRY2,SQSTM1, SRPX, STOM, SYNGR2, SYPL1, TAPBP, TAPBPL, TF, TGOLN2, TIMP1,TIMP2, TIMP3, TIPARP, TM4SF1, TMED10, TMED9, TMEM66, TMX4, TNC, TPP1,TSC22D3, TYR, UBC, UPP1, ZBTB20 and ZBTB38 (oncogenic ICR down, seetable S6); or ANXA1, ATF3, BCL6, CD58, CD9, CTSB, DCBLD2, EMP1, HLA-F,HTATIP2, IL1RAP, ITGA6, KCNN4, KLF4, MT1E, MT1M, MT1X, MT2A, NNMT,PRKCDBP, S100A6 and TSC22D3 (oncogenic ICR down, see FIG. 2B); or A2M,APOD, BCL6, CD44, CD59, CD63, CDH19, CHI3L1, CTSA, CTSB, CTSD, FOS,GPNMB, GRN, HLA-A, HLA-B, HLA-H, ITM2B, LGALS3BP, NEAT1, PDK4, PSAP,SCARB2, SERPINA3, SLC26A2, TAPBPL, TMEM66 and TYR (oncogenic ICR down,see FIG. 10B), wherein said ICR-down signature is downregulated in atumor with a high ICR score and upregulated in a tumor with a low ICRscore.

In certain embodiments, the ICR signature comprises an ICR-up signature,said signature comprising one or more genes selected from the groupconsisting of: ACTB, AEN, ANP32E, ATP5A1, ATP5G2, BZW2, C17orf76-AS1,C1QBP, C20orf112, CA14, CBX5, CCT2, CCT3, CDK4, CFL1, CNRIP1, CRABP2,CS, CTPS1, DCAF13, DCT, DDX39B, DLL3, EEF1G, EIF2S3, EIF3K, EIF4A1,EIF4EBP2, FAM174B, FBL, FBLN1, FOXRED2, FTL, FUS, GABARAP, GAS5, GNB2L1,GPATCH4, GPI, GRWD1, H3F3A, H3F3AP4, HMGA1, HNRNPA1, HNRNPA1P10, HNRNPC,HSPA8, IDH2, ILF2, ISYNA1, ITM2C, KIAA0101, MAGEA4, MDH2, METAP2, MID1,MIR4461, MLLT11, MPZL1, MRPS21, NACA, NCL, NDUFS2, NOLC1, NONO, PA2G4,PABPC1, PAFAH1B3, PFDN2, PFN1, PGAM1, PIH1D1, PPA1, PPIA, PPP2R1A,PSMD4, PTMA, RAN, RBM34, RNF2, RPAIN, RPL10A, RPL11, RPL12, RPL13,RPL13A, RPL13AP5, RPL17, RPL18, RPL18A, RPL21, RPL26, RPL28, RPL29,RPL3, RPL31, RPL36A, RPL37, RPL37A, RPL39, RPL4, RPL41, RPL5, RPL6,RPL8, RPLP0, RPLP1, RPS10, RPS11, RPS12, RPS15A, RPS16, RPS17, RPS17L,RPS18, RPS19, RPS21, RPS23, RPS24, RPS26, RPS27, RPS27A, RPS3, RPS4X,RPS5, RPS6, RPS7, RPS8, RPS9, RPSA, RUVBL2, SAE1, SCD, SCNM1, SERPINF1,SET, SF3B4, SHMT2, SKP2, SLC25A3, SMS, SNAI2, SNHG6, SNRPE, SOX4, SRP14,SSR2, TIMM50, TMC6, TP53, TRPM1, TSR1, TUBA1B, TUBB, TULP4, UBA52,UQCRFS1, UQCRH, USP22, VCY1B, VDAC2, VPS72, YWHAE and ZNF286A (oncogenicICR up, see table S6); or AEN, ATP5A1, C20orf112, CCT2, DCAF13, DDX39B,ISYNA1, NDUFS2, NOLC1, PA2G4, PPP2R1A, RBM34, RNF2, RPL6, RPL21,SERPINF1, SF3B4, SMS, TMC6, VPS72 (oncogenic ICR up, see FIG. 2B); orACTB, ANP32E, CBX5, FUS, HNRNPA1, IDH2, KIAA0101, NCL, PFN1, PPIA, PTMA,RAN, RPLP0, TUBA1B, TUBB and VCY1B (oncogenic ICR up, see FIG. 10B),wherein said ICR-up signature is upregulated in a tumor with a high ICRscore and downregulated in a tumor with a low ICR score. In certainembodiments, the ICR signature is detected in cycling cells.

In another aspect, the present invention provides for a method ofdetecting an immune cell exclusion gene signature in a tumor comprising,detecting in tumor cells obtained from a subject in need thereof theexpression or activity of a malignant cell gene signature comprising:one or more genes or polypeptides selected from the group consisting ofACAT1, ACP5, ACTG1, ADSL, AK2, APP, ASAP1, ATP5D, BANCR, BCAN, BZW2,C17orf76-AS1, C1QBP, C6orf48, CA14, CCT3, CCT6A, CEP170, CHP1, CTPS1,CYC1, DAP3, DCT, DDX21, EDNRB, EEF1D, EEF1G, EEF2, EIF1AX, EIF2S3,EIF3E, EIF3K, EIF3L, EIF4A1, ESRP1, FAM178B, FAM92A1, FTL, GAS5, GNB2L1,GPI, GSTO1, IFI16, ILF2, IMPDH2, LHFPL3-AS1, LOC100190986, LYPLA1,MARCKS, MDH2, MRPL37, MRPS12, MYC, NCL, NF2, NID1, NOLC1, NPM1, NUCKS1,OAT, PABPC1, PAICS, PLTP, PSAT1, PYCARD, RASA3, RPL10, RPL10A, RPL11,RPL12, RPL13, RPL13A, RPL13AP5, RPL14, RPL17, RPL18, RPL18A, RPL28,RPL29, RPL3, RPL30, RPL35, RPL37A, RPL39, RPL4, RPL5, RPL6, RPL7, RPL7A,RPL8, RPLP0, RPLP1, RPS10, RPS11, RPS15, RPS15A, RPS16, RPS17, RPS17L,RPS18, RPS19, RPS2, RPS24, RPS27, RPS3, RPS3A, RPS4X, RPS5, RPS7, RPS8,RPS9, RPSA, RSL1D1, SCD, SERBP1, SERPINF1, SLC19A1, SLC25A5, SLC25A6,SNAI2, SNHG16, SNHG6, SORD, SOX4, TIMM13, TIMM50, TOP1MT, TRAP1, TUBB4A,TXLNA, TYRP1, UCK2, UQCRFS1, ZFAS1, A2M, AGA, AHNAK, ANXA1, APLP2,APOC2, ARF5, ATP1A1, ATP1B1, ATRAID, B2M, C10orf54, C4A, CBLB, CCND3,CD151, CD47, CD58, CD59, CDH19, CHN1, CLU, CPVL, CST3, CTSB, CTSD,CTSL1, DDR1, DPYSL2, DSCR8, DUSP6, DYNLRB1, EMP1, EZR, FAM3C, FGFR1,FYB, GAA, GATSL3, GRN, GSN, HCP5, HLA-B, HLA-C, HLA-F, HLA-H, HSPA1A,HSPA1B, ID2, IFI27L2, ISCU, ITGA3, ITGA7, ITGB3, KCNN4, KRT10,LOC100506190, LTBP3, LYRM9, MAEL, MAP1B, MATN2, MFGE8, MFI2, MIA, MRPS6,MT2A, NDRG1, NFKBIA, NPC1, OCIAD2, PAGES, PERP, PKM, RDH5, S100A13,S100A6, SERPINA1, SERPINA3, SERPINE2, SGCE, SLC26A2, SLC5A3, SNX9,SPON2, THBD, TIMP1, TM4SF1, TMBIM6, TNFSF4, TPP1, TRIML2, TSC22D3,TSPYL2, TXNIP, UBC, XAGE1A, XAGE1B, XAGE1C, XAGE1D and XAGE1E(exclusion, see table S6); or one or more genes or polypeptides selectedfrom the group consisting of ACTG1, ADSL, C17orf76-AS1, C1QBP, CTPS1,EIF2S3, EIF3E, ILF2, NCL, NF2, NOLC1, PABPC1, PAICS, RPL10A, RPL18,RPL6, RPS24, RSL1D1, SERPINF1, SOX4, AHNAK, ANXA1, CCND3, CD151, CD47,CD58, CST3, CTSB, CTSD, EMP1, FGFR1, HLA-C, HLA-F, ITGB3, KCNN4, MIA,MT2A, S100A6, SLC5A3, TIMP1 and TSC22D3 (exclusion, see FIG. 2H); or oneor more genes or polypeptides selected from the group consisting ofC17orf76-AS1, C1QBP, CTPS1, EIF2S3, ILF2, NCL, NOLC1, PABPC1, RPL10A,RPL18, RPL6, RPS24, SERPINF1, SOX4, AHNAK, ANXA1, CCND3, CD151, CD47,CD58, CST3, CTSB, CTSD, EMP1, FGFR1, HLA-C, HLA-F, ITGB3, KCNN4, MIA,MT2A, S100A6, SLC5A3, TIMP1 and TSC22D3 (exclusion, see FIG. 2H).

In certain embodiments, the exclusion signature comprises anexclusion-down signature, said signature comprising one or more genesselected from the group consisting of: A2M, AGA, AHNAK, ANXA1, APLP2,APOC2, ARF5, ATP1A1, ATP1B1, ATRAID, B2M, C10orf54, C4A, CBLB, CCND3,CD151, CD47, CD58, CD59, CDH19, CHN1, CLU, CPVL, CST3, CTSB, CTSD,CTSL1, DDR1, DPYSL2, DSCR8, DUSP6, DYNLRB1, EMP1, EZR, FAM3C, FGFR1,FYB, GAA, GATSL3, GRN, GSN, HCP5, HLA-B, HLA-C, HLA-F, HLA-H, HSPA1A,HSPA1B, ID2, IFI27L2, ISCU, ITGA3, ITGA7, ITGB3, KCNN4, KRT10,LOC100506190, LTBP3, LYRM9, MAEL, MAP1B, MATN2, MFGE8, MFI2, MIA, MRPS6,MT2A, NDRG1, NFKBIA, NPC1, OCIAD2, PAGES, PERP, PKM, RDH5, S100A13,S100A6, SERPINA1, SERPINA3, SERPINE2, SGCE, SLC26A2, SLC5A3, SNX9,SPON2, THBD, TIMP1, TM4SF1, TMBIM6, TNFSF4, TPP1, TRIML2, TSC22D3,TSPYL2, TXNIP, UBC, XAGE1A, XAGE1B, XAGE1C, XAGE1D and XAGE1E(exclusion-down, see table S6); or AHNAK, ANXA1, CCND3, CD151, CD47,CD58, CST3, CTSB, CTSD, EMP1, FGFR1, HLA-C, HLA-F, ITGB3, KCNN4, MIA,MT2A, S100A6, SLC5A3, TIMP1 and TSC22D3 (exclusion-down, see FIG. 2H),wherein said exclusion-down signature is downregulated in a tumor with Tcell exclusion and is upregulated in a tumor with T cell infiltration.

In certain embodiments, the exclusion signature comprises anexclusion-up signature, said signature comprising one or more genesselected from the group consisting of: ACAT1, ACP5, ACTG1, ADSL, AK2,APP, ASAP1, ATP5D, BANCR, BCAN, BZW2, C17orf76-AS1, C1QBP, C6orf48,CA14, CCT3, CCT6A, CEP170, CHP1, CTPS1, CYC1, DAP3, DCT, DDX21, EDNRB,EEF1D, EEF1G, EEF2, EIF1AX, EIF2S3, EIF3E, EIF3K, EIF3L, EIF4A1, ESRP1,FAM178B, FAM92A1, FTL, GAS5, GNB2L1, GPI, GSTO1, IFI16, ILF2, IMPDH2,LHFPL3-AS1, LOC100190986, LYPLA1, MARCKS, MDH2, MRPL37, MRPS12, MYC,NCL, NF2, NID1, NOLC1, NPM1, NUCKS1, OAT, PABPC1, PAICS, PLTP, PSAT1,PYCARD, RASA3, RPL10, RPL10A, RPL11, RPL12, RPL13, RPL13A, RPL13AP5,RPL14, RPL17, RPL18, RPL18A, RPL28, RPL29, RPL3, RPL30, RPL35, RPL37A,RPL39, RPL4, RPL5, RPL6, RPL7, RPL7A, RPL8, RPLP0, RPLP1, RPS10, RPS11,RPS15, RPS15A, RPS16, RPS17, RPS17L, RPS18, RPS19, RPS2, RPS24, RPS27,RPS3, RPS3A, RPS4X, RPS5, RPS7, RPS8, RPS9, RPSA, RSL1D1, SCD, SERBP1,SERPINF1, SLC19A1, SLC25A5, SLC25A6, SNAI2, SNHG16, SNHG6, SORD, SOX4,TIMM13, TIMM50, TOP1MT, TRAP1, TUBB4A, TXLNA, TYRP1, UCK2, UQCRFS1 andZFAS1 (exclusion-up, see table S6); or ACTG1, ADSL, C17orf76-AS1, C1QBP,CTPS1, EIF2S3, EIF3E, ILF2, NCL, NF2, NOLC1, PABPC1, PAICS, RPL10A,RPL18, RPL6, RPS24, RSL1D1, SERPINF1 and SOX4 (exclusion-up, see FIG.2H); or C17orf76-AS1, C1QBP, CTPS1, EIF2S3, ILF2, NCL, NOLC1, PABPC1,RPL10A, RPL18, RPL6, RPS24, SERPINF1 and SOX4 (exclusion-up, see FIG.2H), wherein said exclusion-up signature is upregulated in a tumor withT cell exclusion and is downregulated in a tumor with T cellinfiltration.

In certain embodiments, the method according to any embodiment hereinfurther comprises detecting tumor infiltrating lymphocytes (TIL). Notbeing bound by a theory, detecting tumor infiltration of immune cells isan independent indicator of immunotherapy resistance and progressionfree survival and combining detection of TILs with any of the abovesignatures may increase the prognostic value.

In certain embodiments, the gene signature according to any embodimentherein is detected in a bulk tumor sample, whereby the gene signature isdetected by deconvolution of bulk expression data such that geneexpression is assigned to malignant cells and non-malignant cells insaid tumor sample.

In certain embodiments, detecting the gene signature comprises detectingdownregulation of the down signature and/or upregulation of the upsignature. In certain embodiments, not detecting the gene signaturecomprises detecting upregulation of the down signature and/ordownregulation of the up signature. In certain embodiments, detectingthe signature and/or TILs indicates lower progression free survivaland/or resistance to checkpoint blockade therapy. In certainembodiments, not detecting the signature and/or TILs indicates higherprogression free survival and/or sensitivity to checkpoint blockadetherapy. In certain embodiments, detecting the gene signature indicatesa 10-year survival rate less than 40% and wherein not detecting thesignature indicates a 10-year survival rate greater than 60%.

In certain embodiments, detecting an ICR signature in a tumor furthercomprises detecting in tumor infiltrating lymphocytes (TIL) obtainedfrom the subject in need thereof the expression or activity of a CD8 Tcell gene signature, said signature comprising one or more genes orpolypeptides selected from the group consisting of CEP19, EXO5, FAM153C,FCRL6, GBP2, GBP5, HSPA1B, IER2, IRF1, KLRK1, LDHA, LOC100506083,MBOAT1, SEMA4D, SIRT3, SPDYE2, SPDYE2L, STAT1, STOM, UBE2Q2P3, ACP5,AKNA, BTN3A2, CCDC141, CD27, CDC42SE1, DDIT4, FAU, FKBP5, GPR56, HAVCR2,HLA-B, HLA-C, HLA-F, IL6ST, ITGA4, KIAA1551, KLF12, MIR155HG, MTA2,MTRNR2L1, MTRNR2L3, PIK3IP1, RPL26, RPL27, RPL27A, RPL35A, RPS11, RPS16,RPS20, RPS26, SPOCK2, SYTL3, TOB1, TPT1, TTN, TXNIP, WNK1 and ZFP36L2.In certain embodiments, detecting an ICR signature in a tumor furthercomprises detecting in macrophages obtained from the subject in needthereof the expression or activity of a macrophage gene signature, saidsignature comprising one or more genes or polypeptides selected from thegroup consisting of APOL1, CD274, CSTB, DCN, HLA-DPB2, HLA-DQA1, HLA-G,HSPA8, HSPB1, IL18BP, TMEM176A, UBD, A2M, ADAP2, ADORA3, ARL4C, ASPH,BCAT1, C11orf31, C3, C3AR1, C6orf62, CAPN2, CD200R1, CD28, CD9, CD99,COMT, CREM, CRTAP, CYFIP1, DDOST, DHRS3, EGFL7, EIF1AY, ETS2, FCGR2A,FOLR2, GATM, GBP3, GNG2, GSTT1, GYPC, HIST1H1E, HPGDS, IFI44L, IGFBP4,ITGA4, KCTD12, LGMN, LOC441081, LTC4S, LYVE1, MERTK, METTL7B, MS4A4A,MS4A7, MTSS1, NLRP3, OLFML3, PLA2G15, PLXDC2, PMP22, POR, PRDX2, PTGS1,RNASE1, ROCK1, RPS4Y1, S100A9, SCAMP2, SEPP1, SESN1, SLC18B1, SLC39A1,SLC40A1, SLC7A8, SORL1, SPP1, STAB1, TMEM106C, TMEM86A, TMEM9, TNFRSF1B,TNFRSF21, TPD52L2, ULK3 and ZFP36L2.

In another aspect, the present invention provides for a method ofstratifying cancer patients into a high survival group and a lowsurvival group comprising detecting the expression or activity of an ICRand/or exclusion signature in a tumor according to any embodimentherein, wherein if the signature is detected the patient is in the lowsurvival group and if the signature is not detected the patient is inthe high survival group. The patients in the high survival group may beimmunotherapy responders and patients in the low survival group may beimmunotherapy non-responders.

In another aspect, the present invention provides for a method oftreating a cancer in a subject in need thereof comprising detecting theexpression or activity of an ICR and/or exclusion signature according toany embodiment herein in a tumor obtained from the subject andadministering a treatment, wherein if an ICR and/or exclusion signatureis detected the treatment comprises administering an agent capable ofreducing expression or activity of said signature, and wherein if an ICRand/or exclusion signature is not detected the treatment comprisesadministering an immunotherapy. The agent capable of reducing expressionor activity of said signature may comprise a CDK4/6 inhibitor, a drugselected from Table 3, a cell cycle inhibitor, a PKC activator, aninhibitor of the NFκB pathway, an IGF1R inhibitor, or Reserpine. Theagent capable of reducing expression or activity of said signature maycomprise an agent capable of modulating expression or activity of a geneselected from the group consisting of MAZ, NFKBIZ, MYC, ANXA1, SOX4,MT2A, PTP4A3, CD59, DLL3, SERPINE2, SERPINF1, PERP, EGR1, SERPINA3,SEMA3B, SMARCA4, IFNGR2, B2M, and PDL1. The agent capable of reducingexpression or activity of said signature may comprise an agent capableof targeting or binding to one or more up-regulated secreted or cellsurface exposed ICR and/or exclusion signature genes or polypeptides.The method may further comprise detecting the expression or activity ofan ICR and/or exclusion signature according to any embodiment herein ina tumor obtained from the subject after the treatment and administeringan immunotherapy if said signature is reduced or below a referencelevel. The agent capable of reducing expression or activity of saidsignature may be a CDK4/6 inhibitor. The method may further comprisedetecting the expression or activity of an ICR and/or exclusionsignature according to any embodiment herein in a tumor obtained fromthe subject before the treatment and administering an immunotherapy ifsaid signature is not detected or below a reference level.

In certain embodiments, the method further comprises administering animmunotherapy to the subject administered an agent capable of reducingthe expression or activity of said signature. The immunotherapy maycomprise a check point inhibitor or adoptive cell transfer (ACT). Theadoptive cell transfer may comprise a CAR T cell or activated autologousT cells. The checkpoint inhibitor may comprise anti-CTLA4, anti-PD-L1and/or anti-PD1 therapy.

In another aspect, the present invention provides for a method oftreating a cancer in a subject in need thereof comprising detecting theexpression or activity of an ICR and/or exclusion signature according toany embodiment herein in a tumor obtained from the subject, wherein ifan ICR and/or exclusion signature is detected the treatment comprisesadministering an agent capable of modulating expression or activity ofone or more genes or polypeptides in a network of genes disrupted byperturbation of a gene selected from the group consisting of MAZ,NFKBIZ, MYC, ANXA1, SOX4, MT2A, PTP4A3, CD59, DLL3, SERPINE2, SERPINF1,PERP, EGR1, SERPINA3, SEMA3B, SMARCA4, IFNGR2, B2M, and PDL1.

In another aspect, the present invention provides for a method oftreating a cancer in a subject in need thereof comprising administeringto the subject a therapeutically effective amount of an agent: capableof modulating the expression or activity of one or more ICR and/orexclusion signature genes or polypeptides according to any embodimentherein; or capable of targeting or binding to one or more cell surfaceexposed ICR and/or exclusion signature genes or polypeptides, whereinthe gene or polypeptide is up-regulated in the ICR and/or exclusionsignature; or capable of targeting or binding to one or more receptorsor ligands specific for a cell surface exposed ICR and/or exclusionsignature gene or polypeptide, wherein the gene or polypeptide isup-regulated in the ICR and/or exclusion signature; or comprising asecreted ICR and/or exclusion signature gene or polypeptide, wherein thegene or polypeptide is down-regulated in the ICR and/or exclusionsignature; or capable of targeting or binding to one or more secretedICR and/or exclusion signature genes or polypeptides, wherein the genesor polypeptides are up-regulated in the ICR and/or exclusion signature;or capable of targeting or binding to one or more receptors specific fora secreted ICR and/or exclusion signature gene or polypeptide, whereinthe secreted gene or polypeptide is up-regulated in the ICR and/orexclusion signature; or comprising a CDK4/6 inhibitor, a drug selectedfrom Table 3, a cell cycle inhibitor, a PKC activator, an inhibitor ofthe NFκB pathway, an IGF1R inhibitor, or Reserpine.

In certain embodiments, the agent comprises a therapeutic antibody,antibody fragment, antibody-like protein scaffold, aptamer, protein,CRISPR system or small molecule.

In certain embodiments, the agent capable of targeting or binding to oneor more cell surface exposed ICR and/or exclusion signature polypeptidesor one or more receptors specific for a secreted ICR and/or exclusionsignature gene or polypeptide comprises a CAR T cell capable oftargeting or binding to one or more cell surface exposed ICR and/orexclusion signature genes or polypeptides or one or more receptorsspecific for a secreted ICR and/or exclusion signature gene orpolypeptide.

In certain embodiments, the agent capable of modulating the expressionor activity of one or more ICR and/or exclusion signature genes orpolypeptides comprises a CDK4/6 inhibitor. The CDK4/6 inhibitor maycomprise Abemaciclib.

In certain embodiments, the method further comprises administering animmunotherapy to the subject. The immunotherapy may comprise a checkpoint inhibitor. The checkpoint inhibitor may comprise anti-CTLA4,anti-PD-L1 and/or anti-PD1 therapy.

In another aspect, the present invention provides for a method ofmonitoring a cancer in a subject in need thereof comprising detectingthe expression or activity of an ICR and/or exclusion gene signatureaccording to any embodiment herein in tumor samples obtained from thesubject for at least two time points. The at least one sample may beobtained before treatment. The at least one sample may be obtained aftertreatment.

In certain embodiments, the cancer according to any embodiment herein ismelanoma.

In certain embodiments, the ICR and/or exclusion signature is expressedin response to administration of an immunotherapy.

In another aspect, the present invention provides for a method ofdetecting an ICR signature in a tumor comprising, detecting in tumorcells obtained from a subject in need thereof who has been treated withan immunotherapy the expression or activity of a malignant cell genesignature comprising: a) one or more down regulated genes selected fromthe group consisting of genes associated with coagulation, apoptosis,TNF-α signaling via NFκb, Antigen processing and presentation,metallothionein and IFNGR2; and/or b) one or more up regulated genesselected from the group consisting of genes associated with negativeregulation of angiogenesis and MYC targets.

In another aspect, the present invention provides for a kit comprisingreagents to detect at least one ICR and/or exclusion signature gene orpolypeptide according to any embodiment herein. The kit may comprise atleast one antibody, antibody fragment, or aptamer. The kit may compriseprimers and/or probes for quantitative RT-PCR or fluorescently bar-codedoligonucleotide probes for hybridization to RNA.

In another aspect, the present invention provides for a CD8 T cellspecific cycling signature (see Table S8). In certain embodiments,modulating target genes in this signature can allow boosting T cellproliferation without activating tumor growth. Not being bound by atheory proliferating CD8 T cells express features that are not presentin proliferating malignant cells. In certain embodiments, induction ofoxidative phosphorylation and/or repression of hematopoietic lineagegenes (e.g., CD37, IL11RA, and IL7R) may increase CD8 T cellproliferation without affecting tumor proliferation.

In another aspect, the present invention provides for a method ofdetecting an immunotherapy resistance (ITR) gene signature in a tumorcomprising, detecting in tumor cells obtained from a subject in needthereof the expression or activity of a malignant cell gene signaturecomprising:

a) one or more genes or polypeptides selected from the group consistingof ACOT7, ACSL3, ACTN1, ADAM15, ADI1, AEBP1, AGPAT1, AGRN, AHCY, AIF1L,AKAP12, AKT3, ANXA5, APOA1BP, APOD, APOE, ARL2, ARNT2, ARPC1A, ASPH,ATP1A1, ATP1B1, ATP6V0A1, B3GNT1, BACE2, BAIAP2, BCAN, BIRC7, BTBD3,C11orf24, C17orf89, C1orf198, C1orf21, C1orf85, CALD1, CALU, CAPN3,CAV1, CBR1, CCND1, CCT3, CD151, CD276, CD59, CD63, CD9, CDC42BPA,CDC42EP4, CDH19, CDK2, CDK2AP1, CECR7, CELSR2, CERCAM, CERS2, CHCHD6,CHL1, CHPF, CLDN12, CLIC4, CNIH4, CNN3, CNP, CNPY2, COA3, COL16A1, COMT,CRIP2, CRNDE, CRTAP, CRYAB, CSAG1, CSAG3, CSPG4, CSRP1, CTDSPL, CTHRC1,CTNNAL1, CTNNB1, CTSF, CTSK, CTTN, CYB5R1, CYP27A1, CYSTM1, CYTH3,DAAM2, DCBLD2, DCT, DDR1, DDR2, DIP2C, DLC1, DNAH14, DOCK7, DST, DSTN,DUSP6, ECM1, EDNRB, EFNA5, EIF4EBP1, EMP1, ENTPD6, EPS8, ERBB3, ETV4,ETV5, EVA1A, EXOSC4, FAM127A, FAM127B, FAM167B, FARP1, FARP2, FASN,FKBP10, FKBP4, FKBP9, FN1, FNBP1L, FRMD6, FSTL1, FXYD3, G6PC3, GALE,GCSH, GDF15, GJB1, GLI3, GNG12, GOLM1, GPM6B, GPR143, GPRC5B, GSTA4,GSTP1, GULP1, GYG2, H1F0, HIBADH, HMCN1, HMG20B, HOXB7, HOXC10, HSBP1,HSP90AB1, HSPB1, HSPD1, HSPG2, IFI27, IGF1R, IGFBP7, IGSF11, IGSF3,IGSF8, IMPDH2, ISYNA1, ITFG3, ITGA3, ITGB3, KIRREL, LAMB1, LAMB2, LAMC1,LAPTM4A, LAPTM4B, LDLRAD3, LGALS1, LGALS3BP, LINC00473, LINC00673, LMNA,LOC100126784, LOC100130370, LOC645166, LOXL4, LRP6, MAGEA12, MAGEA2B,MAGEA3, MAGEA6, MAGED1, MAGED2, MAP1B, MARCKSL1, MDK, MFAP2, MFGE8,MFI2, MGST3, MIA, MIF, MITF, MLANA, MLPH, MMP14, MORF4L2, MORN2, MPZL1,MRPL24, MT2A, MTUS1, MXI1, MYH10, MYO10, MYO1D, NAV2, NCKAP1, NDST1,NENF, NES, NGFRAP1, NGRN, NHSL1, NID1, NME1, NME2, NME4, NRP2, NRSN2,NSG1, OSBPL1A, P4HA2, PACSIN2, PAX3, PCDHGC3, PEG10, PFDN2, PFKM, PFN2,PGRMC1, PHB, PHLDB1, PIR, PKNOX2, PLEKHB1, PLK2, PLOD1, PLOD3, PLP1,PLS3, PLXNA1, PLXNB3, PMEL, PMP22, POLR2F, POLR2L, PON2, PPT2, PRAME,PRDX4, PRDX6, PRKCDBP, PROS1, PRSS23, PSMB5, PTGFRN, PTGR1, PTK2,PTPLAD1, PTPRM, PTPRS, PTRH2, PTTG1IP, PYCR1, PYGB, PYGL, QDPR, QPCT,RAB13, RAB17, RAB34, RAB38, RAI14, RBFOX2, RCAN1, RCN1, RCN2, RDX,RGS20, RND3, ROBO1, ROPN1, ROPN1B, RTKN, S100A1, S100A13, S100A16,S100B, SCARB1, SCCPDH, SCD, SDC3, SDC4, SDCBP, SELENBP1, SEMA3B, SEMA3C,SEMA6A, SEPT10, SERPINA3, SERPINE2, SERPINH1, SGCD, SGCE, SHC1, SHC4,SLC19A2, SLC24A5, SLC25A13, SLC25A4, SLC35B2, SLC39A1, SLC39A6, SLC45A2,SLC6A15, SLC7A8, SMARCA1, SNAI2, SNCA, SNHG16, SNRPE, SORT1, SOX10,SOX13, SOX4, SPARC, SPR, SPRY4, SPTBN1, SRPX, SSFA2, ST3GAL4, ST5,ST6GALNAC2, STK32A, STMN1, STXBP1, SYNGR1, TANC1, TBC1D16, TBC1D7,TCEAL4, TEAD1, TENC1, TEX2, TFAP2A, TIMP2, TIMP3, TJP1, TMEM147,TMEM14C, TMEM9, TMEM98, TNFRSF19, TOM1L1, TRIM2, TRIM63, TSC22D1,TSPAN3, TSPAN4, TSPAN6, TTLL4, TUBB2A, TUBB2B, TUBB3, TYR, UBL3, VAT1,VIM, VKORC1, WASL, WBP5, WIPI1, WLS, XAGE1A, XAGE1B, XAGE1C, XAGE1D,XAGE1E, XYLB, YWHAE and ZNF462; or

b) one or more genes or polypeptides selected from FIG. 3C; or

c) one or more genes or polypeptides selected from the group consistingof ABHD2, ACSL4, AHNAK, AHR, AIM2, ANGPTL4, ANXA1, ANXA2, APOD, ATF3,ATP1A1, ATP1B3, BBX, BCL6, BIRC3, BSG, C16orf45, C8orf40, CALU, CARD16,CAV1, CBFB, CCDC109B, CCND3, CD151, CD200, CD44, CD46, CD47, CD58, CD59,CD9, CD97, CDH19, CERS5, CFB, CHI3L2, CLEC2B, CLIC4, COL16A1, COL5A2,CREG1, CRELD1, CRYAB, CSPG4, CST3, CTNNAL1, CTSA, CTSB, CTSD, DCBLD2,DCTN6, EGR1, EMP1, EPDR1, FAM114A1, FAM46A, FCRLA, FN1, FNDC3B, FXYD3,G6PD, GAA, GADD45B, GALNS, GBP2, GEM, GRAMD3, GSTM2, HLA-A, HLA-C,HLA-E, HLA-F, HPCAL1, HSP90B1, HTATIP2, IFI27L2, IFI44, IFI6, IFITM3,IGF1R, IGFBP3, IGFBP7, IL1RAP, ITGA6, ITGB3, ITM2B, JUNB, KCNN4,KIAA1551, KLF4, KLF6, LAMB1, LAMP2, LGALS1, LGALS3BP, LINC00116,LOC100127888, LOXL2, LOXL3, LPL, LXN, MAGEC2, MFI2, MIA, MT1E, MT1F,MT1G, MT1M, MT1X, MT2A, NFE2L1, NFKBIZ, NNMT, NOTCH2, NR4A1, OS9, P4HA2,PDE4B, PELI1, PIGT, PMAIP1, PNPLA8, PPAPDC1B, PRKCDBP, PRNP, PROS1,PRSS23, PSMB9, PSME1, PTPMT1, PTRF, RAMP1, RND3, RNH1, RPN2, S100A10,S100A6, SCCPDH, SERINC1, SERPINA3, SERPINE1, SERPINE2, SLC20A1, SLC35A5,SLC39A14, SLC5A3, SMIM3, SPARC, SPRY2, SQRDL, STAT1, SUMF1, TAP1, TAPBP,TEKT4P2, TF, TFAP2C, TMEM43, TMX4, TNC, TNFRSF10B, TNFRSF12A, TSC22D3,TSPAN31, UBA7, UBC, UBE2L6, XPO7, ZBTB20, ZDHHC5, ZMYM6NB, ACAA2, ADSL,AEN, AHCY, ALDH1B1, ARHGEF1, ARPC5, ATXN10, ATXN2L, B4GALT3, BCCIP, BGN,C10orf32, C16orf88, C17orf76-AS1, C20orf112, CDCA7, CECR5, CPSF1, CS,CTCFL, CTPS1, DLL3, DTD2, ECHDC1, ECHS1, EIF4A1, EIF4EBP2, EIF6, EML4,ENY2, ESRG, FAM174B, FAM213A, FBL, FBLN1, FDXR, FOXRED2, FXN, GALT,GEMIN8, GLOD4, GPATCH4, HDAC2, HMGN3, HSD17B14, IDH2, ILF2, ISYNA1,KIAA0020, KLHDC8B, LMCD1, LOC100505876, LYPLA1, LZTS2, MAZ, METAP2,MID1, MIR4461, MPDU1, MPZL1, MRPS16, MSTO1, MTG1, MYADM, MYBBP1A, MYL6B,NARS2, NCBP1, NDUFAF6, NDUFS2, NF2, NHEJ1, NME6, NNT, NOLC1, NTHL1,OAZ2, OXA1L, PABPC1, PAICS, PAK1IP1, PFN1, POLR2A, PPA1, PRAME, PRDX3,PSTPIP2, PTGDS, PTP4A3, RBM34, RBM4, RPL10A, RPL17, RPP30, RPS3, RPS7,RPSA, RUVBL2, SAMM50, SBNO1, SERPINF1, SKP2, SLC45A2, SMC3, SMG7, SMS,SNAI2, SORD, SOX4, SRCAP, SRSF7, STARD10, TBXA2R, TH005, TIMM22, TIMM23,TMC6, TOMM22, TPM1, TSNAX, TSR1, TSTA3, TULP4, UBAP2L, UCHL5, UROS,VPS72, WDR6, XPNPEP1, XRCC5, YDJC, ZFP36L1, and ZNF286A; or

d) one or more genes or polypeptides selected from the group consistingof AHNAK, AHR, ANXA1, ATP1B3, BBX, BCL6, BIN3, C16orf45, CARD16, CAST,CAV1, CAV2, CD59, CD9, CDH19, CLEC2B, CRYAB, CYSTM1, FAM114A1, FAM46A,FCRLA, FXYD3, G6PD, GBP2, HLA-A, HLA-E, HLA-F, IGF1R, IL1RAP, IL6ST,ITGB1, ITM2B, KCNN4, KLF4, KLF6, LAMP2, LEPROT, LGALS1, LOC100127888,MT1X, MT2A, MVP, NFAT5, NFE2L1, NFKBIZ, PLP2, PROS1, PRSS23, RNF145,S100A10, SEL1L, SERINC1, SERPINA3, SERPINE2, SPRY2, SQRDL, SQSTM1,TAPBP, TF, TMBIM1, TNFRSF10B, TNFRSF12A, UBE2B, and ZBTB20; or

e) one or more genes or polypeptides selected from the group consistingof TM4SF1, ANXA1, MT2A, SERPINA3, EMP1, MIA, ITGA3, CDH19, CTSB,SERPINE2, MFI2, APOC2, ITGB8, S100A6, NNMT, SLC5A3, SEMA3B, TSC22D3,ITGB3, MATN2, CRYAB, PERP, CSPG4, SGCE, CD9, A2M, FGFR1, CST3, DDR1,CD59, DPYSL2, KCNN4, SLC26A2, CD151, SLC39A14, AHNAK, ATP1A1, PROS1,TIMP1, TRIML2, EGR1, TNC, DCBLD2, DUSP4, DUSP6, CD58, FAM3C, ATP1B1,MT1E, TNFRSF12A, FXYD3, SCCPDH, GAA, TIMP3, LEF1-AS1, CAV1, MFGE8,NR4A1, LGALS3, CCND3, CALU, RDH5, APOD, LINC00116, IL1RAP, SERPINA1,NFKBIZ, HSPA1A, PRSS23, MAP1B, ITGA7, PLP2, IGFBP7, GSN, LOXL3, PTRF,LGALS1, IGF1R, SERPINE1, MT1X, ATP1B3, SDC3, ZBTB38, NSG1, FCGR2A, KLF4,EGR3, DAG1, CTSD, CPVL, EEA1, SLC20A1, CLU, GBP2, SPON2, TNFSF4, NPC1,PRKCDBP, HTATIP2, C16orf45, SERPINF1, DCT, SNAI2, PTP4A3, RPS19, BCAN,FOXRED2, FAM174B, TRPM1, ESRP1, PABPC1, CA14, TMC6, C17orf76-AS1,RPL13AP5, TP53, BANCR, RPL28, IDH2, LOC100133445, TYRP1, DLL3,LHFPL3-AS1, SCIN, EIF4EBP2, TIMM50, CD68, GPI, MIR4461, RPS27, C1QBP,EGFL8, RPL21, FAM178B, RPS24, SAE1, KLHDC8B, KCNAB2, RPLP0, SCD, TULP4,IL6R, LINC00439, TSTD1, NF2, TUBB4A, SOX4, RPS3, NAPRT1, RPS6, LIMD2,CDKN2A, PTGDS, ISYNA1, ARHGDIB, CNRIP1, H3F3A, TBXA2R, PSTPIP2,SERPINB9, TMEM204, SORD, RPS5, CDH3, RPL18A, RPL8, VPS53, RBM34, FES,ESRG, RPS7, HSD17B14, TTC39A, FBLN1, SLC45A2, AEN, ACP5, BCL11A, CHP1,XIST, MAZ, FAM92A1, CTPS1, ASAP1, RPL6, MARCKS, MAGEA4, NPL, RPS16,NENF, SLC19A1, FTL, RNF2, MYBBP1A, PPAP2C, GRWD1, SKP2, WDR81, DCUN1D2,LAMP2 and MPZL1; or

f) one or more genes or polypeptides selected from the group consistingof TM4SF1, MT2A, SERPINA3, CDH19, SERPINE2, CRYAB, SGCE, A2M, DDR1,CD59, DPYSL2, DUSP6, MFGE8, NFKBIZ, and PRSS23; or

g) one or more genes or polypeptides selected from the group consistingof SERPINA3, MT2A, SERPINF1, SERPINE2, SOX4, DDR1, CD59, DUSP6, PERP,SEMA3B, PTP4A3, BANCR, DLL3, and LAMP2; or

h) one or more genes or polypeptides selected from the group consistingof MT2A, MT1E, MT1X, MT1M, MT1F, MT1G, MTX1 and MTG1.

In one embodiment, the ITR signature further comprises one or more genesor polypeptides selected from the group consisting of IFNGR2, B2M, andPDL1.

In one embodiment, said ITR signature comprises a post-immunotherapysignature-down (PIT-down) module, said module comprising one or moregenes selected from the group consisting of: ABHD2, ACSL4, AHNAK, AHR,AIM2, ANGPTL4, ANXA1, ANXA2, APOD, ATF3, ATP1A1, ATP1B3, BBX, BCL6,BIRC3, BSG, C16orf45, C8orf40, CALU, CARD16, CAV1, CBFB, CCDC109B,CCND3, CD151, CD200, CD44, CD46, CD47, CD58, CD59, CD9, CD97, CDH19,CERS5, CFB, CHI3L2, CLEC2B, CLIC4, COL16A1, COL5A2, CREG1, CRELD1,CRYAB, CSPG4, CST3, CTNNAL1, CTSA, CTSB, CTSD, DCBLD2, DCTN6, EGR1,EMP1, EPDR1, FAM114A1, FAM46A, FCRLA, FN1, FNDC3B, FXYD3, G6PD, GAA,GADD45B, GALNS, GBP2, GEM, GRAMD3, GSTM2, HLA-A, HLA-C, HLA-E, HLA-F,HPCAL1, HSP90B1, HTATIP2, IFI27L2, IFI44, IFI6, IFITM3, IGF1R, IGFBP3,IGFBP7, IL1RAP, ITGA6, ITGB3, ITM2B, JUNB, KCNN4, KIAA1551, KLF4, KLF6,LAMB1, LAMP2, LGALS1, LGALS3BP, LINC00116, LOC100127888, LOXL2, LOXL3,LPL, LXN, MAGEC2, MFI2, MIA, MT1E, MT1F, MT1G, MT1M, MT1X, MT2A, NFE2L1,NFKBIZ, NNMT, NOTCH2, NR4A1, 0S9, P4HA2, PDE4B, PELI1, PIGT, PMAIP1,PNPLA8, PPAPDC1B, PRKCDBP, PRNP, PROS1, PRSS23, PSMB9, PSME1, PTPMT1,PTRF, RAMP1, RND3, RNH1, RPN2, S100A10, S100A6, SCCPDH, SERINC1,SERPINA3, SERPINE1, SERPINE2, SLC20A1, SLC35A5, SLC39A14, SLC5A3, SMIM3,SPARC, SPRY2, SQRDL, STAT1, SUMF1, TAP1, TAPBP, TEKT4P2, TF, TFAP2C,TMEM43, TMX4, TNC, TNFRSF10B, TNFRSF12A, TSC22D3, TSPAN31, UBA7, UBC,UBE2L6, XPO7, ZBTB20, ZDHHC5 and ZMYM6NB; or TM4SF1, ANXA1, MT2A,SERPINA3, EMP1, MIA, ITGA3, CDH19, CTSB, SERPINE2, MFI2, APOC2, ITGB8,S100A6, NNMT, SLC5A3, SEMA3B, TSC22D3, ITGB3, MATN2, CRYAB, PERP, CSPG4,SGCE, CD9, A2M, FGFR1, CST3, DDR1, CD59, DPYSL2, KCNN4, SLC26A2, CD151,SLC39A14, AHNAK, ATP1A1, PROS1, TIMP1, TRIML2, EGR1, TNC, DCBLD2, DUSP4,DUSP6, CD58, FAM3C, ATP1B1, MT1E, TNFRSF12A, FXYD3, SCCPDH, GAA, TIMP3,LEF1-AS1, CAV1, MFGE8, NR4A1, LGALS3, CCND3, CALU, RDH5, APOD,LINC00116, IL1RAP, SERPINA1, NFKBIZ, HSPA1A, PRSS23, MAP1B, ITGA7, PLP2,IGFBP7, GSN, LOXL3, PTRF, LGALS1, IGF1R, SERPINE1, MT1X, ATP1B3, SDC3,ZBTB38, NSG1, FCGR2A, KLF4, EGR3, DAG1, CTSD, CPVL, EEA1, SLC20A1, CLU,GBP2, SPON2, TNFSF4, NPC1, PRKCDBP, HTATIP2, and C16orf45; or an mICRdown gene in FIG. 3C, wherein said PIT-down module is downregulated in atumor resistant to immunotherapy and upregulated in a tumor sensitive toimmunotherapy as compared to a reference level.

In one embodiment, said ITR signature comprises a post-immunotherapysignature-up (PIT-up) module, said module comprising one or more genesselected from the group consisting of: ACAA2, ADSL, AEN, AHCY, ALDH1B1,ARHGEF1, ARPC5, ATXN10, ATXN2L, B4GALT3, BCCIP, BGN, C10orf32, C16orf88,C17orf76-AS1, C20orf112, CDCA7, CECR5, CPSF1, CS, CTCFL, CTPS1, DLL3,DTD2, ECHDC1, ECHS1, EIF4A1, EIF4EBP2, EIF6, EML4, ENY2, ESRG, FAM174B,FAM213A, FBL, FBLN1, FDXR, FOXRED2, FXN, GALT, GEMIN8, GLOD4, GPATCH4,HDAC2, HMGN3, HSD17B14, IDH2, ILF2, ISYNA1, KIAA0020, KLHDC8B, LMCD1,LOC100505876, LYPLA1, LZTS2, MAZ, METAP2, MID1, MIR4461, MPDU1, MPZL1,MRPS16, MSTO1, MTG1, MYADM, MYBBP1A, MYL6B, NARS2, NCBP1, NDUFAF6,NDUFS2, NF2, NHEJ1, NME6, NNT, NOLC1, NTHL1, OAZ2, OXA1L, PABPC1, PAICS,PAK1IP1, PFN1, POLR2A, PPA1, PRAME, PRDX3, PSTPIP2, PTGDS, PTP4A3,RBM34, RBM4, RPL10A, RPL17, RPP30, RPS3, RPS7, RPSA, RUVBL2, SAMM50,SBNO1, SERPINF1, SKP2, SLC45A2, SMC3, SMG7, SMS, SNAI2, SORD, SOX4,SRCAP, SRSF7, STARD10, TBXA2R, TH005, TIMM22, TIMM23, TMC6, TOMM22,TPM1, TSNAX, TSR1, TSTA3, TULP4, UBAP2L, UCHL5, UROS, VPS72, WDR6,XPNPEP1, XRCC5, YDJC, ZFP36L1 and ZNF286A; or SERPINF1, DCT, SNAI2,PTP4A3, RPS19, BCAN, FOXRED2, FAM174B, TRPM1, ESRP1, PABPC1, CA14, TMC6,C17orf76-AS1, RPL13AP5, TP53, BANCR, RPL28, IDH2, LOC100133445, TYRP1,DLL3, LHFPL3-AS1, SCIN, EIF4EBP2, TIMM50, CD68, GPI, MIR4461, RPS27,C1QBP, EGFL8, RPL21, FAM178B, RPS24, SAE1, KLHDC8B, KCNAB2, RPLP0, SCD,TULP4, IL6R, LINC00439, TSTD1, NF2, TUBB4A, SOX4, RPS3, NAPRT1, RPS6,LIMD2, CDKN2A, PTGDS, ISYNA1, ARHGDIB, CNRIP1, H3F3A, TBXA2R, PSTPIP2,SERPINB9, TMEM204, SORD, RPS5, CDH3, RPL18A, RPL8, VPS53, RBM34, FES,ESRG, RPS7, HSD17B14, TTC39A, FBLN1, SLC45A2, AEN, ACP5, BCL11A, CHP1,XIST, MAZ, FAM92A1, CTPS1, ASAP1, RPL6, MARCKS, MAGEA4, NPL, RPS16,NENF, SLC19A1, FTL, RNF2, MYBBP1A, PPAP2C, GRWD1, SKP2, WDR81, DCUN1D2,and MPZL1; or an mICR up gene in FIG. 3C, wherein said PIT-up module isupregulated in a tumor resistant to immunotherapy and downregulated in atumor sensitive to immunotherapy as compared to a reference level.

Detecting an immunotherapy resistance gene signature in a tumor mayfurther comprise detecting in tumor infiltrating lymphocytes (TIL)obtained from the subject in need thereof the expression or activity ofa CD8 T cell gene signature, said signature comprising one or more genesor polypeptides selected from the group consisting of APOBEC3G, CBLB,CCL4, CCL4L1, CCL4L2, CCL5, CD27, CD8A, CD8B, CST7, CTSW, CXCL13, CXCR6,DTHD1, DUSP2, EOMES, FASLG, FCRL3, GBP5, GZMA, GZMB, GZMH, GZMK, HCST,HLA-A, HLA-B, HLA-H, ID2, IFNG, IL2RB, KLRC3, KLRC4, KLRC4-KLRK1, KLRD1,KLRK1, LAG3, LSP1, LYST, NKG7, PDCD1, PRF1, PSTPIP1, PYHIN1, RARRES3,SH2D1A, SH2D2A, TARP, TIGIT, TNFRSF9 and TOX.

Detecting an immunotherapy resistance gene signature in a tumor mayfurther comprise detecting in tumor infiltrating lymphocytes (TIL)obtained from the subject in need thereof the expression or activity ofa CD4 T cell gene signature, said signature comprising one or more genesor polypeptides selected from the group consisting of AIM1, ANK3, AQP3,CAMK4, CCR4, CCR8, CD28, CD40LG, DGKA, EML4, FAAH2, FBLN7, FKBP5,FLT3LG, FOXP3, FXYD5, IL6R, IL7R, ITGB2-AS1, JUNB, KLRB1, LEPROTL1,LOC100128420, MAL, OXNAD1, PBXIP1, PIK3IP1, PIM2, PRKCQ-AS1, RORA,RPL35A, RPL4, RPL6, RPS15A, RPS27, RPS28, 6-Sep, SLAMF1, SORL1, SPOCK2,SUSD3, TCF7, TMEM66, TNFRSF18, TNFRSF25, TNFRSF4, TNFSF8, TRABD2A,TSC22D3 and TXK.

Detecting an immunotherapy resistance gene signature in a tumor mayfurther comprise detecting in macrophages obtained from the subject inneed thereof the expression or activity of a macrophage gene signature,said signature comprising one or more genes or polypeptides selectedfrom the group consisting of AIF1, ALDH2, ANPEP, C15orf48, C1orf162,C1QA, C1QB, C1QC, C3AR1, CCR1, CD14, CD163, CD300A, CD300C, CD300LF,CD33, CD86, CFP, CLEC10A, CLEC12A, CLEC4A, CLEC5A, CMKLR1, CSF1R,CSF2RB, CSF3R, CSTA, CXCL9, CXCR2P1, DSC2, FAM26F, FBP1, FCER1G, FCGR1A,FCGR1B, FCGR1C, FCGR3A, FCGR3B, FCN1, FOLR2, FPR1, FPR2, FPR3, GGTA1P,GNA15, GPR84, HCK, HK3, IGSF6, IL1B, IL1RN, IL4I1, ITGAM, KYNU, LGALS2,LILRA1, LILRA2, LILRA3, LILRA4, LILRB2, LILRB4, LILRB5, LST1, MAFB,MARCO, MNDA, MRC1, MS4A4A, MS4A6A, MSR1, NCF2, OLR1, P2RY13, PILRA,PLAU, PLBD1, PLXDC2, PRAM1, RAB20, RAB31, RASSF4, RBM47, RGS18, S100A8,S100A9, SECTM1, SIGLEC1, SIGLEC7, SIGLEC9, SLAMF8, SLC31A2, SLC43A2,SLC7A7, SLC8A1, SLCO2B1, SPI1, STAB1, TBXAS1, TFEC, TGFBI, TLR2, TLR4,TLR8, TMEM176A, TMEM176B, TNFSF13, TNFSF13B, TREM2, TYROBP, VSIG4 andZNF385A.

Detecting an immunotherapy resistance gene signature in a tumor mayfurther comprise detecting in B cells obtained from the subject in needthereof the expression or activity of a B cell gene signature, saidsignature comprising one or more genes or polypeptides selected from thegroup consisting of ADAM19, AKAP2, BACH2, BANK1, BCL11A, BLK, CD19,CD1C, CD22, CD79A, CD79B, CLEC17A, CNR2, COL19A1, COL4A3, CPNE5, CR2,CXCR5, EBF1, ELK2AP, FAM129C, FAM177B, FCER2, FCRL1, FCRL2, FCRL5,FCRLA, HLA-DOB, IGJ, IGLL1, IGLL3P, IGLL5, KIAA0125, KIAA0226L,LOC283663, MS4A1, P2RX5, PAX5, PNOC, POU2AF1, POU2F2, RASGRP3, SEL1L3,SNX29P1, ST6GAL1, STAP1, SWAP70, TCL1A, TMEM154 and VPREB3.

The gene signature may be detected in a bulk tumor sample, whereby thegene signature is detected by deconvolution of bulk expression data suchthat gene expression is assigned to malignant cells and non-malignantcells in said tumor sample.

Detecting the ITR gene signature may comprise detecting downregulationof the PIT-down module and/or upregulation of the PIT-up module. Notdetecting the ITR gene signature may comprise detecting upregulation ofthe PIT-down module and/or downregulation of the PIT-up module. Thedetecting an ITR gene signature may indicates a 10-year survival rateless than 40% and wherein not detecting said signature may indicate a10-year survival rate greater than 60%. The detecting an ITR genesignature may indicate exclusion of T cells from a tumor and wherein notdetecting said signature may indicate infiltration of T cells in atumor.

In another aspect, the present invention provides for a method ofstratifying cancer patients into a high survival group and a lowsurvival group comprising detecting the expression or activity of animmunotherapy resistance gene signature in a tumor, wherein if animmunotherapy resistance gene signature is detected the patient is inthe low survival group and if an immunotherapy resistance gene signatureis not detected the patient is in the high survival group. The patientsin the high survival group may be immunotherapy responders and patientsin the low survival group may be immunotherapy non-responders.

In another aspect, the present invention provides for a method oftreating a cancer in a subject in need thereof comprising detecting theexpression or activity of an immunotherapy resistance gene signatureaccording to any of claims 1 to 10 in a tumor obtained from the subjectand administering a treatment, wherein if an immunotherapy resistancesignature is detected the treatment comprises administering an agentcapable of reducing expression or activity of said signature, andwherein if an immunotherapy resistance signature is not detected thetreatment comprises administering an immunotherapy. The agent capable ofreducing expression or activity of said signature may comprise a drugselected from Table 3, a PKC activator, an inhibitor of the NFκBpathway, an IGF1R inhibitor, or Reserpine. The agent capable of reducingexpression or activity of said signature may comprise an agent capableof modulating expression or activity of a gene selected from the groupconsisting of MAZ, NFKBIZ, MYC, ANXA1, SOX4, MT2A, PTP4A3, CD59, DLL3,SERPINE2, SERPINF1, PERP, EGR1, SERPINA3, SEMA3B, SMARCA4, IFNGR2, B2M,and PDL1. The agent capable of reducing expression or activity of saidsignature may comprise an agent capable of targeting or binding to oneor more up-regulated secreted or cell surface exposed immunotherapyresistance signature genes or polypeptides. The method may furthercomprise detecting the expression or activity of an immunotherapyresistance gene signature in a tumor obtained from the subject after thetreatment and administering an immunotherapy if said signature is notdetected. The method may further comprise administering an immunotherapyto the subject administered an agent capable of reducing the expressionor activity of said signature. The immunotherapy may comprise a checkpoint inhibitor or adoptive cell transfer (ACT). The adoptive celltransfer may comprise a CAR T cell or activated autologous T cells. Thecheckpoint inhibitor may comprise anti-CTLA4, anti-PD-L1 and/or anti-PD1therapy.

In another aspect, the present invention provides for a method oftreating a cancer in a subject in need thereof comprising detecting theexpression or activity of an immunotherapy resistance gene signatureaccording to any embodiment herein in a tumor obtained from the subject,wherein if an immunotherapy resistance signature is detected thetreatment comprises administering an agent capable of modulatingexpression or activity of one or more genes or polypeptides in a networkof genes disrupted by perturbation of a gene selected from the groupconsisting of MAZ, NFKBIZ, MYC, ANXA1, SOX4, MT2A, PTP4A3, CD59, DLL3,SERPINE2, SERPINF1, PERP, EGR1, SERPINA3, SEMA3B, SMARCA4, IFNGR2, B2M,and PDL1.

In another aspect, the present invention provides for a method oftreating a cancer in a subject in need thereof comprising administeringto the subject a therapeutically effective amount of an agent: capableof modulating the expression or activity of one or more immunotherapyresistance signature genes or polypeptides; or capable of targeting orbinding to one or more cell surface exposed immunotherapy resistancesignature genes or polypeptides, wherein the gene or polypeptide isup-regulated in the ITR signature; or capable of targeting or binding toone or more receptors or ligands specific for a cell surface exposedimmunotherapy resistance signature gene or polypeptide, wherein the geneor polypeptide is up-regulated in the ITR signature; or comprising asecreted immunotherapy resistance signature gene or polypeptide, whereinthe gene or polypeptide is down-regulated in the ITR signature; orcapable of targeting or binding to one or more secreted immunotherapyresistance signature genes or polypeptides, wherein the genes orpolypeptides are up-regulated in the ITR signature; or capable oftargeting or binding to one or more receptors specific for a secretedimmunotherapy resistance signature gene or polypeptide, wherein thesecreted gene or polypeptide is up-regulated in the ITR signature; orcomprising a drug selected from Table 3, a PKC activator, an inhibitorof the NFκB pathway, an IGF1R inhibitor, or Reserpine. The agent capableof modulating the expression or activity of one or more immunotherapyresistance signature genes or polypeptides may comprise a CDK4/6inhibitor. The CDK4/6 inhibitor may comprise Abemaciclib. The method mayfurther comprise administering an immunotherapy to the subject. Theimmunotherapy may comprise a check point inhibitor. The checkpointinhibitor may comprise anti-CTLA4, anti-PD-L1 and/or anti-PD1 therapy.Not being bound by a theory, the CDK4/6 inhibitor may sensitize asubject to checkpoint blockade therapy. The agent may comprise atherapeutic antibody, antibody fragment, antibody-like protein scaffold,aptamer, protein, CRISPR system or small molecule. The agent capable oftargeting or binding to one or more cell surface exposed immunotherapyresistance signature polypeptides or one or more receptors specific fora secreted immunotherapy resistance signature gene or polypeptide maycomprise a CAR T cell capable of targeting or binding to one or morecell surface exposed immunotherapy resistance signature genes orpolypeptides or one or more receptors specific for a secretedimmunotherapy resistance signature gene or polypeptide.

In another aspect, the present invention provides for a method ofmonitoring a cancer in a subject in need thereof comprising detectingthe expression or activity of an immunotherapy resistance gene signatureaccording to any embodiment herein in tumor samples obtained from thesubject for at least two time points. The at least one sample may beobtained before treatment. The at least one sample may be obtained aftertreatment.

The cancer according to any embodiment may be melanoma. The ITR genesignature may be expressed in response to administration of animmunotherapy.

In another aspect, the present invention provides for a method ofdetecting T cell infiltration of a tumor comprising detection inmalignant cells expression or activity of one or more genes selectedfrom the group consisting of: HLA-C, FGFR1, ITGB3, CD47, AHNAK, CTSD,TIMP1, SLC5A3, CST3, CD151, CCND3, MIA, CD58, CTSB, S100A6, EMP1, HLA-F,TSC22D3, ANXA1, KCNN4 and MT2A; or A2M, AEBP1, AHNAK, ANXA1, APOC2,APOD, APOE, ATP1A1, ATP1B1, C4A, CAPN3, CAV1, CD151, CD59, CD63, CDH19,CRYAB, CSPG4, CSRP1, CST3, CTSB, CTSD, DAG1, DDR1, DUSP6, ETV5, EVA1A,FBXO32, FCGR2A, FGFR1, GAA, GATSL3, GJB1, GRN, GSN, HLA-B, HLA-C, HLA-F,HLA-H, IFI35, IGFBP7, IGSF8, ITGA3, ITGA7, ITGB3, LAMP2, LGALS3, LOXL4,LRPAP1, LY6E, LYRM9, MATN2, MFGE8, MIA, MPZ, MT2A, MTRNR2L3, MTRNR2L6,NPC1, NPC2, NSG1, PERP, PKM, PLEKHB1, PROS1, PRSS23, PYGB, RDH5, ROPN1,S100A1, S100A13, S100A6, S100B, SCARB2, SCCPDH, SDC3, SEMA3B, SERPINA1,SERPINA3, SERPINE2, SGCE, SGK1, SLC26A2, SLC5A3, SPON2, SPP1, TIMP1,TIMP2, TIMP3, TM4SF1, TMEM255A, TMX4, TNFSF4, TPP1, TRIML2, TSC22D3,TXNIP, TYR, UBC and WBP2; or HLA-A, HLA-B, HLA-C, B2M, TAPBP, IFI27,IFI35, IRF4, IRF9 and STAT2; or B2M, CTSB, CTSL1, HLA-B/C/F, HSPA1A,HSPA1B, NFKBIA and CD58, wherein detection indicates sensitivity toimmunotherapy.

In another aspect, the present invention provides for a method ofdetecting T cell exclusion of a tumor comprising detection in malignantcells expression or activity of one or more genes selected from thegroup consisting of: SERPINF1, RPL6, NOLC1, RSL1D1, ILF2, SOX4, ACTG1,C17orf76-AS1, PABPC1, RPS24, ADSL, C1QBP, PAICS, CTPS1, NF2, EIF2S3,RPL18 and RPL10A; or AHCY, BZW2, CCNB1IP1, CCT6A, EEF2, EIF3B, GGCT,ILF3, IMPDH2, MDH2, MYBBP1A, NT5DC2, PAICS, PFKM, POLD2, PTK7, SLC19A1,SMARCA4, STRAP, TIMM13, TOP1MT, TRAP1 and USP22; or MYC, STRAP andSMARCA4; or MYC, SNAI2 and SOX4, wherein detection indicates resistanceto immunotherapy.

In another aspect, the present invention provides for a method ofdetecting an immunotherapy resistance gene signature in a tumorcomprising, detecting in tumor cells obtained from a subject in needthereof who has been treated with an immunotherapy the expression oractivity of a malignant cell gene signature comprising: one or more downregulated genes selected from the group consisting of genes associatedwith coagulation, apoptosis, TNF-α signaling via NFκb, Antigenprocessing and presentation, metallothionein and IFNGR2; and/or one ormore up regulated genes selected from the group consisting of genesassociated with negative regulation of angiogenesis and MYC targets.

In another aspect, the present invention provides for a kit comprisingreagents to detect at least one immunotherapy resistance signature geneor polypeptide according to the present invention. The kit may compriseat least one antibody, antibody fragment, or aptamer. The kit maycomprise primers and/or probes for quantitative RT-PCR or fluorescentlybar-coded oligonucleotide probes for hybridization to RNA.

It is noted that in this disclosure and particularly in the claimsand/or paragraphs, terms such as “comprises”, “comprised”, “comprising”and the like can have the meaning attributed to it in U.S. Patent law;e.g., they can mean “includes”, “included”, “including”, and the like;and that terms such as “consisting essentially of” and “consistsessentially of” have the meaning ascribed to them in U.S. Patent law,e.g., they allow for elements not explicitly recited, but excludeelements that are found in the prior art or that affect a basic or novelcharacteristic of the invention.

These and other aspects, objects, features, and advantages of theexample embodiments will become apparent to those having ordinary skillin the art upon consideration of the following detailed description ofillustrated example embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

The following detailed description, given by way of example, but notintended to limit the invention solely to the specific embodimentsdescribed, may best be understood in conjunction with the accompanyingdrawings.

FIG. 1 illustrates the study design and T cell analysis of ICR. (A)Overview. 31 samples from patients with metastatic melanoma (discoverycohort) were profiled by scRNA-sequencing (left), of which 15 were TN,15 had ICI resistance (ICR) and one had clinical benefit (CB).Signatures were tested in two validation cohorts collected independently(right), with bulk RNA-seq of melanoma tumors from 112 patients whounderwent biopsies prior to receiving pembrolizumab (anti-PD-1;cohort 1) and from 26 patients, 12 with matched pre-treatment andpost-progression (ICR) biopsies (cohort 2). (B-C) Distinct profiles ofmalignant and non-malignant cells. Shown are tSNE plots of single cellprofiles (dots) from malignant (B) or non-malignant (C) cells, shaded bypost-hoc annotation (materials and methods) or by patient. (D) Variationin T cells ICR. Shown is a tSNE plot of CD8 T cells that Applicantsgenerated based on the genes of the tICR signatures, with cells shadedby treatment category (right), overall expression (OE) of the tICRsignature (middle), and clonality (right). Larger dots: cells from large(>20 cells) clones. (E) Similar relationship between exhaustion andcytotoxicity signatures in TN and ICR CD8 T cells. For each cell (dot),the exhaustion (y axis) and cytotoxicity (x axis) scores are shown(materials and methods).: TN;: ICR;: CB. Cells from the CB patient havelower than expected exhaustion scores. (F) CD8 T cell clones. Shown isthe distribution of clone sizes. Tumors with large (>20 cells) clonesare marked. (G) Expanded clones have higher tICR expression. Box plotsshow the distribution of tICR OE scores (y axis) in CD8 T-cells frompatients stratified by clinical context and by overall clonality level.Left: only CD8 T-cells with reconstructed TCRs are shown; Right: onlyCD8 T-cells that were not from the three ICR patients with major clonalexpansion are shown (right). Box-plots: the middle line represents themedian; box edges are the 25^(th) and 75^(th) percentiles, and whiskersrepresent the most extreme points that do not exceed ±IQR*1.5; pointsbeyond the distance are plotted as single points. (H) CD8 T cellspecific cell-cycle program. Shown are the distribution of OE scores forthe CD8 specific cell cycle program in malignant cells (left) and CD8 Tcells (right). The p-values were computed by comparing the cycling andnon-cycling cells in each cell type with a one-sided t-test.

FIG. 2 illustrates the Malignant cell ICR programs. (A) Robustclassification by the oncogenic-ICR signature. Left: Box-plot shows thedistribution of OE scores for the oncogenic-ICR signature in malignantcells from ICR (blue) and TN (grey) patients, when obtained in across-validation (CV) procedure and tested on withheld data. Middleline: median; box edges: 25^(th) and 75^(th) percentiles, whiskers: mostextreme points that do not exceed ±IQR*1.5; further outliers are markedindividually. Right: Receiver Operating Characteristic (ROC) curve ofthe performances of different signatures in classifying cells as ICR orTN; the CV oncogenic-ICR signature was obtained by leave-one (patient)out CV; the first and second Area under the curve (AUC) values are forclassification of cells and samples, respectively. (B) Genes in theoncogenic-ICR program. Heatmap shows the (centered and scaled)expression of the top 40 oncogenic-ICR-up and oncogenic-ICR-down genes(columns) across the malignant cells (rows), sorted by TN or ICR tumors(shaded bar, left) and clustered within each class. Leftmost bar:cycling and non-cycling cells within each group. Right: The OE of theoncogenic-ICR signature for each cell. (C) Differentially expressed genesets in ICR vs. TN malignant cells. Box-plots (formatted as in (A)) showthe distribution of OE scores for each signature in malignant cells fromICR vs. TN tumors. (D-E) Inverse relationship of the oncogenic-ICR-downand -up programs. Shown are the OE scores of the oncogenic-ICR-down(y-axis) and oncogenic-ICR-up (x-axis) programs in (D) the single cellprofiles from TN (grey) and ICR (blue) tumors, and in (E) lesions ofcutaneous (grey) and uveal melanoma. The Pearson correlation coefficient(r) and p-value are marked. (F) Workflow for identification of theexclusion signatures. (G-H) Congruence between the oncogenic-ICR andexclusion programs. (G) Violin plots of the distribution of OE scores ofexclusion signatures across malignant cells from ICR (blue) and TN(grey) patients. (H) Left: Heat map of the (centered and scaled)expression of the 40 most differentially expressed exclusion-up andexclusion-down (black) genes (columns) in the malignant cells (rows),sorted by ICR and TN tumors (left shaded bar) and clustered withinclass. Leftmost shaded bar labels cycling and non-cycling (black) cellswithin each group. Gene names in the oncogenic-ICR-up oroncogenic-ICR-down signatures (table S6) are marked by shading,respectively. Right: OE scores of the exclusion signature in each cell.

FIG. 3 illustrates that the uICR program has immune evasion properties,and can be reversed by CDK4/6 inhibition. (A-C) Reversal of resistanceprograms by a CDK4/6 inhibitor, abemaciclib. (A) Significance (y axis,−log₁₀(p-value), Wilcoxon rank sum test) of induction (dark green) orrepression (light green) of each signatures in tumors from abemaciclibtreated mice compared to vehicle (31). (B) Distribution of uICR OEscores in breast cancer cell lines (M361, MCF and M453) treated withabemaciclib (“abe”) or with DMSO vehicle (“con”). Box-plots: the middleline represents the median; box edges are the 25^(th) and 75^(th)percentiles, and whiskers represent the most extreme points that do notexceed ±IQR*1.5; points beyond the distance are plotted as singlepoints. (C) The relative expression of the 40 most differentiallyexpressed uICR genes (rows) in abemaciclib-treated (green) and control(purple) breast cancer cells lines (columns). Expression values arenormalized according to the cell-line specific expression in the controlstate or denote over- or under-expression, respectively. Bottom: OEscores of the uICR signature for each cell line. (D) Higher uICR scoresin uveal melanoma. Shown are the distributions of OE scores of the uICRprogram in cutaneous (black) vs. uveal melanoma tumors from TCGA, scoredafter filtering TME contributions (materials and methods). P-value:t-test. (E) Suppression of cell-cell interactions in ICR. Bar plots showfor each malignant signature (x-axis) the number of genes (y-axis, top)in the signature that can engage in a physical interaction with othercell types and the corresponding statistical enrichment (y-axis,−log₁₀(P-value), hypergeometric test, bottom). Values above the dashedline are statistically significant.

FIG. 4 illustrates that the resistance signatures in malignant cells areprognostic and predictive in validation cohorts. (A) Resistancesignatures predict melanoma patient survival based in bulk RNA-seq fromTCGA (37). Kaplan-Meier (KM) plots are stratified by high (top 25%), low(bottom 25%), or intermediate (neither high nor low) expression of therespective signature. Pc p-values test if the signature further enhancesthe predictive power of models with T-cell infiltration levels as acovariate. See FIG. 11 for additional signatures. (B, C) Resistancesignatures distinguish clinical benefit (CB) and non-CB in mouse modelsand melanoma patients. Box plots show the distribution of the OE scoreof the uICR in bulk RNA-Seq from a lung cancer mouse model treated withanti-CTLA-4 therapy (35) (B) or from biopsies of melanoma patients priorto treatment with pembrolizumab (5). Middle line: median; box edges:25th and 75th percentiles, whiskers: most extreme points that do notexceed ±IQR*1.5; further outliers are marked individually. P-value:one-sided t-test. (D-F) Resistance signatures predict melanoma patientoutcomes following pembrolizumab treatment from pre-treatment RNA-Seq inan independent cohort of 112 patients. (D) KM plots of progression-freesurvival (PFS) for the 104 patients in the cohort with available PFSdata, when the patients are stratified by high (top 25%), low (bottom25%), or intermediate (neither high nor low) expression of therespective signature. Prediction is enhanced when controlling for cellcycle as a confounder (two right plots, materials and methods). SeeFIGS. 12 to 13. (E) Bar plot shows predictive value for PFS for the 104patients as in (D) with a COX regression model that accounts forinferred T-cell infiltration levels (−log₁₀(p-value), x axis). Lightblue bars: enhances PFS; grey bars: reduces PFS. Bars with black borderdenote the new signatures identified in this study for malignantresistance. Dashed line: p<0.05. Resistance signatures are significantlymore predictive compared to others (P=3.37*10⁻⁶, Wilcoxon-ranksumtest)). (F) Distribution of OE scores (y axis) of each signature in thepre-treatment bulk RNA-Seq profiles, showing patients with eitherintrinsic resistance (Non-CB, n=49) or with clinical benefit (CB, n=39),with the latter also further stratified based on duration of response(CB<6 mo, n=5; 6 mo<CB<1 year, n=9; CB>1 year, n=25). Twenty-fourpatients with unknown response or stable disease are not shown here (seeFIG. 14). Distinctions are enhanced when accounting for inferred T-cellinfiltration levels (right). P1 and P2 are the one-sided t-test p-valueobtained when comparing the non-CB patients to the CB or CB>1 yrpatients, respectively. The AUC at the top was obtained when predictinglong-term CB (CB>1 yr) in all patients with a recorder response (n=101).Box plots formatted as in (B). (G) Box-plots show the distribution of OEscores (y axis) of each signature in the pre-treatment bulk RNA-Seqprofiles, for patients with complete response (CR, n=14), partialresponse (PR, n=25), or progressive disease (PD, n=49). P is theone-sided t-test p-value obtained when comparing the CR patients to thePR and PD patients. The AUC at the top was obtained when predicting CRin all patients with a recorder response (n=101). (H) Bar plot showspredictive value for predicting complete response with the differentsignatures (−log₁₀(t-test p-value), x-axis) in 101 patients with arecorded response. Light blue bars: positive impact; grey bars: negativeimpact. Bars with black border denote the new signatures identified inthis study for malignant resistance. Dashed line: p=0.05. Resistancesignatures are significantly more predictive compared to othersignatures (P=1.64*10-8, Wilcoxon ranksum test). AUC values are markednext to the bar for each significant association. (I) Model for ICRbased on this study.

FIG. 5 illustrates the classification of malignant and non-malignantcells. (A) Inferred large-scale CNVs distinguish malignant (right) fromnonmalignant (left) cells. Shown are the inferred CNVs (amplification,blue, deletion) along the chromosomes (x axis) for cells (y axis) in tworepresentative tumors. (B-E) Congruence between different assignmentmethods. (B) Each plot shows the relation between two differentscorings, by showing for CD45⁻ cells the distribution of scores (y axis)by one scheme, stratified to two categories by another scheme. CNV:inference of malignant and non-malignant CD45⁻ cells as in (A, materialsand methods); signature based: assignment of CD45⁻ cells as malignant orstroma by scoring the corresponding expression signatures (materials andmethods); differential similarity to melanoma: assignment of CD45⁻ cellsas malignant or non-malignant by similarity to bulk melanoma tumorscompared to normal tissue. Middle line: median; box edges: 25th and 75thpercentiles; whiskers: most extreme points that do not exceed ±IQR*1.5;points beyond the distance: single points. (C) Distribution ofCNV-R-score for cells identified as malignant and non-malignant. TheCNV-R-score of a cell is defined as the Spearman correlation coefficient(r) between the cell's CNV profile and its tumor's inferred CNV profile(materials and methods). (D) The distribution of CNV-R-scores acrosseach identified cell type. (E) The CNV-R-score (y axis) at each overallCNV signal (materials and methods) for malignant and non-malignantcells; Non-malignant cells with values that exceed the dashed lines wereconsidered unresolved and were omitted from further analyses.

FIG. 6 illustrates non-malignant cells. Shown are tSNE plots of allnon-malignant cells (dots), shaded by (A) OE scores (bar) ofwell-established cell type markers (table S3), or (B) detection of CD4or CD8 (CD8A or CD8B).

FIG. 7 illustrates cell type specific ICR signatures. Left panels:Box-plots show the distribution of OE scores for the ICR signature ineach cell type in ICR (blue) and TN (grey) patients. Middle line:median; box edges: 25th and 75th percentiles; whiskers: most extremepoints that do not exceed ±IQR*1.5; points beyond the distance: singlepoints. Middle and right panels: Receiver Operating Characteristic (ROC)curves of the performances of different signatures in classifying cells(middle) or samples (left) as ICR or TN. (A) Malignant cells, (B) CD4 Tcells, (C) CD8 T cells, (D) B cells, (E) macrophages.

FIG. 8 illustrates the shift in the balance of cytotoxicity andexhaustion states in CD8 T-cells in the patient with CB. (A) Thedistribution of expression levels of each of five key checkpoint genesin CD8 T cells from ICR, TN, and CB tumors. (B) Distinct relationshipbetween exhaustion and cytotoxicity signatures in CD8 T cells from a CBpatient. For each cell (dot) shown are the cytotoxicity (x-axis) andexhaustion (y-axis) scores (materials and methods), using differentexhaustion signatures from (1) and (17). TN; ICR; CB. Cells from the CBpatient have lower than expected exhaustion scores (p-values,hypergeometric test materials and methods).

FIG. 9 illustrates clonal expansion of CD8 T cells. (A) TCRreconstruction. Shown is the fraction (y-axis) of T-cells with one (α orβ), both or no TCR chain reconstructed at full length (materials andmethods). (B) Variation in CD8 T cell expansion across tumors. Violinplots show the distribution of estimated proportions of CD8 T cellclones in each tumor. Tumors are shaded by treatment group. The tumorsof ICR patients have higher T-cell clonal expansion (P=3.2*10⁻²,one-sided Wilcoxon ranksum test). (C,D) Persistence of clones over timein one patient (Mel75). Shown are the number (C) and relativeproportions (D) of cells in each clone for two post-ICI lesionscollected, a year apart, from patient Mel75.

FIG. 10 illustrates the relationship between the malignant ICR programand cell cycle. (A, B) Higher ICR in cycling cells. (A) Box plots of thedistribution of OE scores of the oncogenic-ICR signatures (y-axis) incycling and non-cycling cells from ICR and TN tumors (x-axis). Themiddle line represents the median; box edges are the 25^(th) and 75^(th)percentiles, and whiskers represent the most extreme points that do notexceed ±IQR*1.5; points beyond the distance are plotted as singlepoints. (B) Heatmap of the expression of ICR-up (bar) and down (blackbar) genes (rows) that are also induced (repressed) in cycling vs.non-cycling malignant cells. Cells (columns) are sorted by TN and ICRtumors and clustered within each set (bar on top); the cells' cyclingstatus in each category is marked by the bar on top. Bottom: OncogenicICR signature score (y axis) in each cell (x axis). (C) Abemaciclibrepresses the uICR program in breast cancer cell lines. Heatmap of therelative expression of all the uICR genes (rows) in Abemaciclib-treatedand control breast cancer cells lines (columns), based on the data in(24). Gene expression is relative to the basal expression level in eachcell line. Bottom: OE scores (y axis) of the uICR signature for eachcell line (x axis).

FIG. 11 illustrates that the resistance signatures score in TCGA tumorspredict survival of melanoma patients. Kaplan-Meier (KM) plotsstratified by high, intermediate or low OE of the respective signaturein bulk RNA-Seq of TCGA tumors. Pc p-values test if the signaturefurther enhances the predictive power of models with inferred T-cellinfiltration levels as a covariate.

FIG. 12 illustrates that the resistance signature scores inpre-treatment biopsies predict response to anti-PD-1 therapy in anindependent cohort. KM plots of progression-free survival (PFS) for the104 of 112 patients in validation cohort 1 with PFS data, with patientsstratified by high, intermediate and low OE score of the respectivesignature. Pc p-values test if the signature further enhances thepredictive power of models with inferred T cell infiltration levels as acovariate.

FIG. 13 illustrates that the predictive performance of resistancesignatures is enhanced when controlling for the cell cycle. KM plots ofprogression-free survival (PFS) for the 104 of 112 patients invalidation cohort 1 with PFS data, with patients stratified by high,intermediate and low OE score of the respective, after controlling forcell cycle as a confounding factor (materials and methods).

FIG. 14 illustrates the expression of the resistance signatures in 101melanoma patients, stratified according to their clinical response topembrolizumab. Distribution of OE scores (y axis) of each signature inthe pre-treatment bulk RNA-Seq profiles, showing overall 101patientswith complete response (CR, n=14), partial response or stable disease(PR/SD, n=38), or progressive disease (PD, n=49). P is the one-sidedt-test p-value obtained when comparing the CR patients to the PR, SD andPD patients. AUC is also marked on top. Middle line: median; box edges:25^(th) and 75^(th) percentiles; whiskers: most extreme points that donot exceed ±IQR*1.5.

FIG. 15 illustrates pan-cancer analysis of the resistance signatures.Box-plots of the distribution of OE scores (x-axis) of the uICRsignature in bulk RNA-seq profiles of 9,559 tumors across 33 cancertypes (y-axis) from TCGA either scored (A) “as-is” or (B) with aregression-based process to control for TME-related signals (materialsand methods). Middle line: median; box edges: 25th and 75th percentiles;whiskers: most extreme points that do not exceed ±IQR*1.5; points beyondthe distance: single points.

FIG. 16 illustrates that an unbiased analysis reveals a malignant cellstate linked to ICR.

FIG. 17 illustrates an overview of the patients analyzed.

FIG. 18 illustrates the separation of immunotherapy treated anduntreated tumors by Principle Component (PC) analysis.

FIG. 19 illustrates the correlation between the resistance signature andpatients that are naïve or resistant to immunotherapy.

FIG. 20 illustrates a leave-one-out cross validation analysis.

FIG. 21 illustrates mutual exclusive expression of the ITR up and downgenes across malignant cells, and their anti-correlation in TCGA.

FIG. 22 illustrates the correlation between the resistance signature andMHC-I expression.

FIG. 23 illustrates the association of metallothionein expression andtreated and untreated subjects.

FIG. 24 illustrates the association of the resistance signature withprognosis.

FIG. 25 illustrates the resistance signature compared to othersingle-cell based signatures.

FIG. 26 illustrates that the ITR signature is predictive of eventualoutcome in both mouse and human data.

FIG. 27 illustrates the association of complete responders andnon-complete responders to genes up-regulated post-treatment withimmunotherapy.

FIG. 28 illustrates the association of complete responders andnon-complete responders to genes down-regulated post-treatment withimmunotherapy.

FIG. 29 illustrates that malignant cells ITR signatures have higherexclusion signatures and treatment naive malignant cells have higherinfiltration signatures.

FIG. 30 illustrates analysis of CD8 T cells.

FIG. 31 illustrates analysis of CD8 T cells.

FIG. 32 illustrates analysis of CD8 T cells.

FIG. 33 illustrates that the CD8 ITR signature is strongly associatedwith clonal expansion.

FIG. 34 illustrates an interaction map of genes in the ITR signature andimmune and stromal genes.

FIG. 35 illustrates the number of interactions between differentiallyexpressed malignant genes and immune and stromal genes.

FIG. 36 illustrates ITR versus T cell scores in different cancers.

FIG. 37 illustrates ITR scores in two melanomas.

FIG. 38 illustrates tSNE analysis of ER+ metastatic breast cancer usingsingle nuclei RNA-seq (snRNA-seq) on fresh and frozen tissue samples.

FIG. 39 illustrates tSNE analysis of 22 colon cancer samples usingscRNA-seq.

FIG. 40 illustrates that the expanded T cell state is highly correlatedwith the overall T cell infiltration level of tumors in an independentlung cancer cohort (Table S11).

FIG. 41 illustrates that CDK4/6 inhibitors sensitize melanoma cells.

FIG. 42 illustrates that CDK4/6 inhibitors induce markers ofdifferentiation, senescence and immunogenicity in melanoma.

FIG. 43 illustrates that CDK4/6 inhibitors eliminate a resistantsubpopulation of melanoma cells.

FIG. 44. Identification of a T cell exclusion program in malignantcells. (A) Study overview. 31 tumors from melanoma patients (discoverycohort) were profiled by scRNA-seq (left, tan) and integratedanalytically with bulk RNA-Seq data from TCGA (473 melanoma tumors). Thediscovered program was tested in two validation cohorts of bulk RNA-Seqcollected independently (right). (B) Analysis approach to discovermalignant cell programs associated with immune cell infiltration orexclusion. (C-D) Distinct profiles of malignant and nonmalignant cells.tSNE plots of single cell profiles (dots) from malignant (C) ornonmalignant (D) cells, shaded by post-hoc annotation (Methods, D left)or by tumor (C, D right). (E) Exclusion program. Expression (centeredand scaled; bar) of the top genes (columns) in the exclusion programacross the malignant cells (rows), sorted by untreated or post-treatmenttumors (blue/grey bar, left) and clustered within each class. Leftmostbar: cycling and non-cycling cells within each group. Right: The overallexpression (Methods) of the exclusion program in each cell. See alsoFIG. 51 and Tables S1-S3.

FIG. 45. Exclusion and resistance programs characterizing individualmalignant cells from patients who failed immunotherapy. (A)Post-treatment program in malignant cells. Left: The Overall expression(Methods) of the post-treatment program in malignant cells frompost-treatment (blue) and untreated (grey) patients, when obtained in across-validation (CV) procedure and tested on withheld data. Middleline: median; box edges: 25^(th) and 75^(th) percentiles, whiskers: mostextreme points that do not exceed ±IQR*1.5; further outliers are markedindividually. Right: Receiver Operating Characteristic (ROC) curve ofthe performances of different programs in classifying cells aspost-treatment or untreated; the CV post-treatment signature wasobtained by leave-one (patient) out CV; the first and second Area Underthe Curve (AUC) values are for classification of cells and samples,respectively. (B) Significant overlap between the exclusion andpost-treatment programs. Venn diagram of the number of genes in eachprogram and in their overlap. P-value: hypergeometric test. (C) Programgenes. Expression (centered and scaled, bar) of the top genes (columns)in the post-treatment program across the malignant cells (rows), sortedby untreated or post-treatment tumors (bar, left) and clustered withineach class. Leftmost bar: cycling and non-cycling cells within eachgroup. Right: overall expression of the post-treatment program in eachcell. (D) Repressed and induced processes. The distribution of overallexpression scores of differentially expressed gene sets in malignantcells from post-treatment (blue) and untreated (gray) tumors (formattedas in (A)). (E) The exclusion program is higher in post-treatmentmalignant cells. The distribution of overall expression scores of theexclusion program in malignant cells from post-treatment (blue) anduntreated (gray) tumors. See also Tables S6 and S9.

FIG. 46. The resistance program is a coherently regulated module thatrepresses cell-cell interactions. (A) The immune resistance program ishigher in uveal vs. cutaneous melanoma. The distribution of overallexpression scores of the immune resistance program in cutaneous vs.uveal melanoma tumors from TCGA, scored after filtering tumormicroenvironment contributions (Methods). (B) Cell-cell interactiongenes are repressed in the immune resistance program. The number ofgenes (y axis, top) in each part of the program encoding proteins thatengage in a physical interaction with other cell types and thesignificance of the corresponding enrichment (y axis, −log₁₀(P-value),hypergeometric test, bottom). Values above the dashed line arestatistically significant. (C-D) Co-regulation of the immune resistanceprogram. (C) The overall expression of the induced (x axis) andrepressed (y axis) parts of the immune resistance programs in eachmalignant cell (top, scRNA-seq data) and in cutaneous melanoma tumors(bottom, TCGA RNA-Seq data, after filtering tumor microenvironmentsignals). The Pearson correlation coefficient (r) and p-value aremarked. (D) Gene-gene Pearson correlation coefficients (bar) between thegenes in the resistance program, across individual malignant cells fromthe same tumor (top, average coefficient) or across cutaneous melanomatumors from TCGA skin (bottom, after filtering tumor microenvironmenteffects). See also FIG. 52.

FIG. 47. The resistance program is associated with the cold niche insitu. (A-B) Multiplex imaging relates resistance program genes to hot orcold niches. Malignant cells expressing high or low/moderate proteinlevels of HLA-A (A) and c-Jun (B) and their proximity to CD3⁺ T cells(blue) or CD3⁺CD8⁺ T cells (cyan) in three representative tumors. (C)Congruence of multiplex protein and scRNA-seq profiles. Left and middle:tSNE plots of co-embedding of cells from the scRNA-seq data and theimages of a specific tumor (Mel112; others shown in FIG. 53), with cellsshaded by clusters (top left), data source (bottom left), and source andcell type (right). Right: Log-odds ratio (bar, Methods) assessing foreach pair of cell types (rows, columns) if they are assigned to the samecluster significantly more (>0) or less (<0) than expected by chance.See also FIG. 53.

FIG. 48. The resistance program is prognostic and predictive invalidation cohorts. (A) The program predicts melanoma patient survivalbased on bulk RNA-Seq from TCGA (Akbani et al., 2015). Kaplan-Meier (KM)plots stratified by high (top 25%), low (bottom 25%), or intermediate(remainder) expression of the respective program subset. P: COXregression p-value; Pc: COX regression p-value that tests if the programfurther enhances the predictive power of a model with inferred T cellinfiltration levels as a covariate. (B, C) Resistance signaturesdistinguish responders and non-responders in mouse models and melanomapatients. The distribution of overall expression of the resistanceprogram in bulk RNA-Seq from (B) a lung cancer mouse model treated withanti-CTLA-4 therapy (Lesterhuis et al., 2015) or (C) biopsies ofmelanoma patients collected prior to treatment with pembrolizumab (Hugoet al., 2016). Middle line: median; box edges: 25^(th) and 75^(th)percentiles, whiskers: most extreme points that do not exceed ±IQR*1.5;further outliers are marked individually. (D-F) The program predictsmelanoma patient outcomes following pembrolizumab treatment frompre-treatment RNA-Seq in an independent cohort of 112 patients. (D) KMplots of progression-free survival (PFS) for the 104 patients in thecohort with available PFS data, stratified by high (top 25%), low(bottom 25%), or intermediate (remainder) expression of the respectiveprogram subset. (E) Predictive value for PFS (−log₁₀(p-value), x axis,COX regression model that accounts for inferred T cell infiltrationlevels) for the 104 patients in (D). Blue/grey bars: positive/negativecorrelation between expression and PFS. Black border: subsets of theresistance program. Dashed line: p=0.05. (F) Overall expression of theresistance program (y axis) in the pre-treatment bulk RNA-Seq profilesof patients with intrinsic resistance (Non-CB, n=49) or clinical benefit(CB, n=39), latter further stratified by response duration (CB<6 mo,n=5; 6 mo<CB<1 year, n=9; CB>1 year, n=25). Twenty four patients withunknown response or stable disease are not shown here. P1 and P2:one-tailed t-test p-value when comparing the non-CB patients to the CBor to CB>1 yr patients, respectively. AUC for predicting CB>1 yr in allpatients with a recorded response (n=101) is denoted. Box plotsformatted as in (B). (G) Overall expression values of the resistanceprogram (y axis) in the pre-treatment bulk RNA-Seq profiles of patientswith complete response (CR, n=14), partial response (PR, n=25), orprogressive disease (PD, n=49). P: one-tailed t-test p-value comparingCR patients to PR and PD patients. AUC for predicting CR in all patientswith a recorded response (n=101). (H) Predictive value of differentsignatures for complete response (−log₁₀(t-test p-value), x axis) in 101patients with a recorded response. Blue/grey bars: expression associatedwith CR/non-CR, respectively. Black border: subsets of the resistanceprogram. Dashed line: p=0.05. AUC values are marked next to the bar foreach significant association. See also FIGS. 54, 55, 57 and Table S10.

FIG. 49. The resistance program can be reversed by CDK4/6 inhibition.(A-C) Impact on breast cancer tumors and cell lines. (A) Significance (yaxis, −log₁₀(p-value), Wilcoxon rank sum test) of induction (dark green)or repression (light green) of the program subsets in breast cancertumors from abemaciclib treated mice compared to vehicle (Goel et al.,2017). (B) Overall expression of the program in breast cancer cell lines(M361, MCF and M453) treated with abemaciclib (“abe”) or with DMSOvehicle (“con”). Middle line: median; box edges: 25^(th) and 75^(th)percentiles, whiskers: most extreme points that do not exceed ±IQR*1.5;further outliers are marked individually. P-value: paired t-test. (C)Expression of 40 program genes (columns; shaded bar) that were mostdifferentially expressed in abemaciclib-treated vs. control breastcancer cells lines (rows). Expression is normalized to each cell line'scontrol. Right: overall expression values of the program for each cellline. (D-G) CDK4/6 inhibition reverses the program in melanoma celllines and induces the SASP. (D,E) tSNE plots of 4,024 IGR137 (D) and7,340 UACC257 (E) melanoma cells, shaded by (left to right): treatment,clusters, or the expression of a cell cycle signature, resistanceprogram, MITF signature, SASP signature and DNMT1. (F) Concentration(pg/ml, y axis) of secreted chemokines in the supernatant of melanomacells treated for 7 days with abemaciclib (500 nM) or with DMSO control.**P<0.01, ***P<0.001 t-test. (G) Senescence associatedalpha-galactosidase activity (green) and morphological alterations inmelanoma cells treated for 10 days with abemaciclib (500 nM, right) vs.DMSO control (left). See also FIG. 56 and Table S12.

FIG. 50. Immune resistance model. Malignant cells that evade the immunesystem have a unique transcriptional state, which distinguishes betweenresponders and non-responders to immunotherapy. This state is tightlylinked to the exclusion of T cells from the tumor, the repression ofSASP and cell-cell communication routes, and the inhibition of cytokinesecretion. CDK4/6 inhibition can reverse this state in malignant cells.

FIG. 51. Assignment of cells into cell types by scRNA-seq; related toFIG. 44. (A) Inferred large-scale CNVs distinguish malignant fromnonmalignant cells. Shown are the inferred CNVs (amplification,deletion) along the chromosomes (x axis) for cells (y axis) in tworepresentative tumors partitioned as malignant (left) or nonmalignant(right) by CD45 sorting and transcriptional features. (B-E) Congruencebetween different assignment methods. (B) Each plot shows the relationbetween two different scorings, by showing for CD45⁻ cells thedistribution of scores (y axis) by one scheme, stratified to twocategories by another scheme. CNV: inference of malignant andnonmalignant CD45⁻ cells (as in A, Methods); signature based: assignmentof CD45⁻ cells as malignant or stroma by scoring the correspondingexpression signatures (Methods); differential similarity to melanoma:assignment of CD45⁻ cells as malignant or nonmalignant by similarity tobulk melanoma tumors compared to normal tissue. Middle line: median; boxedges: 25^(th) and 75^(th) percentiles, whiskers: most extreme pointsthat do not exceed ±IQR*1.5; further outliers are marked individually.(C) Distribution of CNV-R-scores for cells called as malignant ornonmalignant. The CNV-R-score of a cell is the Spearman correlationcoefficient (r) between the cell's CNV profile and its tumor's inferredCNV profile (Methods). (D) The distribution of CNV-R-scores across eachidentified cell subset. Box plots as in (B). (E) The CNV-R-score (yaxis) vs. the overall CNV signal (x axis, Methods) for malignant andnonmalignant cells; Nonmalignant cells with values that exceed thedashed lines were considered unresolved and were omitted from furtheranalyses. (F-G) tSNE plots of all nonmalignant cells (dots), shaded by(F) overall expression (bar) of well-established cell type markers(Table S3), or (G) detection of CD4 or CD8 (CD8A or CD8B).

FIG. 52. Co-variation of the resistance signature genes across singlecells within each tumor; related to FIG. 46. Gene-gene Pearsoncorrelation coefficients (bar) between the genes in the resistanceprogram, across individual malignant cells from each specific tumor (aslabeled). Genes are sorted in the same order in all heatmaps (and inFIG. 46D). The consistent intra-tumor correlation suggests sharedregulation.

FIG. 53. Integrative analysis of scRNA-seq and spatial multiplex proteinIHC data; related to FIG. 47. (A-D) Integrative analysis of scRNA-seqand CyCIF multiplex protein data from each of four tumors: (A) Mel79,(B) Mel80, (C) Mel74, and (D) Mel89. Left: tSNE plots of co-embedding ofcells from scRNA-seq and images of each tumors, with cells shaded by(from left): clusters, data source, or source and cell type. Right:Log-odds ratio (bar, Methods) assessing for each pair of cell types(rows, columns) if they are assigned to the same cluster significantlymore (>0) or less (<0) than expected by chance.

FIG. 54. The immune resistance program predicts survival of TCGAmelanoma patients; related to FIG. 48. Kaplan-Meier (KM) plotsstratified by high, intermediate or low Over expression of therespective signature in bulk RNA-Seq of TCGA tumors. P: COX regressionp-value; Pc: COX regression p-value that tests if the program furtherenhances the predictive power of a model with inferred T cellinfiltration levels as a covariate.

FIG. 55. The immune resistance program predicts response to anti-PD-1therapy in an independent cohort; related to FIG. 48. (A-E) KM plots ofprogression-free survival (PFS) for the 104 of 112 patients invalidation cohort 2 with PFS data, with patients stratified by high,intermediate and low over expression values of the respective signature,after controlling for cell cycle as a confounding factor (Methods). Pcp-values test if the signature further enhances the predictive power ofmodels with inferred T cell infiltration levels as a covariate. (F)Distribution of overall expression values (y axis) of each signature inthe pre-treatment bulk RNA-Seq profiles, showing overall 101 patientswith either complete response (CR, n=14), partial response/stabledisease (PR/SD, n=38), or progressive disease (PD, n=49). P is theone-sided t-test p-value obtained when comparing CR patients vs. PR, SDand PD patients. AUC is also marked on top. Middle line: median; boxedges: 25^(th) and 75^(th) percentiles, whiskers: most extreme pointsthat do not exceed ±IQR*1.5; further outliers are marked individually.

FIG. 56. Relationship between the resistance program and cell cycle;related to FIG. 49. (A, B) Higher expression of the resistance programin cycling cells. (A) Distribution of overall expression values of theresistance program (y axis) in cycling (grey) and non-cycling (blue)cells from either post-treatment or untreated tumors (x axis). Solidline: mean of the respective distribution; dashed line: mean across allmalignant cells. (B) Expression of genes from the resistance program(rows) that are also differentially expressed in cycling vs. non-cyclingmalignant cells. Cells (columns) are sorted by untreated andpost-treatment tumors and clustered within each set (bar on top); thecells' cycling status in each category is marked by the bar on top. (C)Abemaciclib represses the resistance program in breast cancer celllines. The relative expression of all genes in the resistance program(rows) in abemaciclib-treated and control breast cancer cells lines(columns), based on the data in (Goel et al., 2017). Expression levelsare relative to the basal expression level in each cell line. Bottom:overall expression (y axis) of the resistance program in each cell line(x axis).

FIG. 57. Pan-cancer analysis of the resistance program; related to FIG.48. (A-B) Overall expression of the resistance program (x axis) in 9,559tumors from 33 cancer types (y axis) from TCGA. In (B) aregression-based approach controls for tumor microenvironment-relatedsignals (Methods). Middle line: median; box edges: 25th and 75thpercentiles, whiskers: most extreme points that do not exceed ±IQR*1.5;further outliers are marked individually.

DETAILED DESCRIPTION OF THE EXAMPLE EMBODIMENTS General Definitions

Unless defined otherwise, technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this disclosure pertains. Definitions of common termsand techniques in molecular biology may be found in Molecular Cloning: ALaboratory Manual, 2^(nd) edition (1989) (Sambrook, Fritsch, andManiatis); Molecular Cloning: A Laboratory Manual, 4^(th) edition (2012)(Green and Sambrook); Current Protocols in Molecular Biology (1987) (F.M. Ausubel et al. eds.); the series Methods in Enzymology (AcademicPress, Inc.): PCR 2: A Practical Approach (1995) (M. J. MacPherson, B.D. Hames, and G. R. Taylor eds.): Antibodies, A Laboratory Manual (1988)(Harlow and Lane, eds.): Antibodies A Laboratory Manual, 2^(nd) edition2013 (E. A. Greenfield ed.); Animal Cell Culture (1987) (R. I. Freshney,ed.); Benjamin Lewin, Genes IX, published by Jones and Bartlet, 2008(ISBN 0763752223); Kendrew et al. (eds.), The Encyclopedia of MolecularBiology, published by Blackwell Science Ltd., 1994 (ISBN 0632021829);Robert A. Meyers (ed.), Molecular Biology and Biotechnology: aComprehensive Desk Reference, published by VCH Publishers, Inc., 1995(ISBN 9780471185710); Singleton et al., Dictionary of Microbiology andMolecular Biology 2nd ed., J. Wiley & Sons (New York, N.Y. 1994), March,Advanced Organic Chemistry Reactions, Mechanisms and Structure 4th ed.,John Wiley & Sons (New York, N.Y. 1992); and Marten H. Hofker and Janvan Deursen, Transgenic Mouse Methods and Protocols, 2^(nd) edition(2011).

As used herein, the singular forms “a”, “an”, and “the” include bothsingular and plural referents unless the context clearly dictatesotherwise.

The term “optional” or “optionally” means that the subsequent describedevent, circumstance or substituent may or may not occur, and that thedescription includes instances where the event or circumstance occursand instances where it does not.

The recitation of numerical ranges by endpoints includes all numbers andfractions subsumed within the respective ranges, as well as the recitedendpoints.

The terms “about” or “approximately” as used herein when referring to ameasurable value such as a parameter, an amount, a temporal duration,and the like, are meant to encompass variations of and from thespecified value, such as variations of +/−10% or less, +/−5% or less,+/−1% or less, and +1-0.1% or less of and from the specified value,insofar such variations are appropriate to perform in the disclosedinvention. It is to be understood that the value to which the modifier“about” or “approximately” refers is itself also specifically, andpreferably, disclosed.

Reference throughout this specification to “one embodiment”, “anembodiment,” “an example embodiment,” means that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present invention. Thus,appearances of the phrases “in one embodiment,” “in an embodiment,” or“an example embodiment” in various places throughout this specificationare not necessarily all referring to the same embodiment, but may.Furthermore, the particular features, structures or characteristics maybe combined in any suitable manner, as would be apparent to a personskilled in the art from this disclosure, in one or more embodiments.Furthermore, while some embodiments described herein include some butnot other features included in other embodiments, combinations offeatures of different embodiments are meant to be within the scope ofthe invention. For example, in the appended claims, any of the claimedembodiments can be used in any combination.

All publications, published patent documents, and patent applicationscited in this application are indicative of the level of skill in theart(s) to which the application pertains. All publications, publishedpatent documents, and patent applications cited herein are herebyincorporated by reference to the same extent as though each individualpublication, published patent document, or patent application wasspecifically and individually indicated as being incorporated byreference.

Overview

Embodiments disclosed herein provide methods and compositions fordetecting and modulating an immunotherapy resistance gene signature incancer. Embodiments disclosed herein also provide for diagnosing,prognosing, monitoring and treating tumors based on detection of animmunotherapy resistance gene signature.

As used herein, the immunotherapy resistance signature is referred to as“ITR”, “immunotherapy resistance signature”, “ICR”, “immune checkpointinhibitor resistance”, “mICR”, “malignant immune checkpoint inhibitorresistance”, “PIT”, “post-immunotherapy”, oncogenic-ICR″, “unified-ICR”,“uICR”, “uICR-up”, “uICR-down”, “refined uICR”, “immune resistant,“refined immune resistant”, “post treatment”, “exclusion-up”, or“exclusion-down”. All of these terms may be used in reference to a genesignature in malignant cells from a subject that is resistant to immunecheckpoint inhibitors (ICI). In regards to the exclusion signatures,these signatures refer to signatures in malignant cells that correlateto immune cell exclusion. In other words, exclusion-up refers to genesthat are upregulated in malignant cells and that are correlated withexclusion, while exclusion-down refer to genes downregulated inmalignant cells that are correlated with exclusion. In certainembodiments, exclusion-down refers to genes upregulated when there isimmune cell infiltration and thus can be referred to as the infiltrationsignature. In regards to “oncogenic ICR”, “mICR”, “malignant immunecheckpoint inhibitor resistance”, “Post-treatment”, “PIT”, or“post-immunotherapy”, these terms all refer to genes differentiallyexpressed in malignant cells after immunotherapy. “Immune resistance,“unified-ICR” or “uICR” refers to all genes in the exclusion signatureand post treatment signature. The “refined uICR” and “refined immuneresistant” are shortened lists from the immune resistance signature thatinclude the best performing genes from the exclusion and post treatmentsignatures for predicting immunotherapy sensitivity. In regards to CD8 Tcells “tICR” refers to T cell immune checkpoint inhibitor resistancesignature.

As used herein the term “cancer-specific survival” refers to thepercentage of patients with a specific type and stage of cancer who havenot died from their cancer during a certain period of time afterdiagnosis. The period of time may be 1 year, 2 years, 5 years, etc.,with 5 years being the time period most often used. Cancer-specificsurvival is also called disease-specific survival. In most cases,cancer-specific survival is based on causes of death listed in medicalrecords.

As used herein the term “relative survival” refers to a method used toestimate cancer-specific survival that does not use information aboutthe cause of death. It is the percentage of cancer patients who havesurvived for a certain period of time after diagnosis compared to peoplewho do not have cancer.

As used herein the term “overall survival” refers to the percentage ofpeople with a specific type and stage of cancer who have not died fromany cause during a certain period of time after diagnosis.

As used herein the term “disease-free survival” refers to the percentageof patients who have no signs of cancer during a certain period of timeafter treatment. Other names for this statistic are recurrence-free orprogression-free survival.

As used herein a “signature” may encompass any gene or genes, protein orproteins, or epigenetic element(s) whose expression profile or whoseoccurrence is associated with a specific cell type, subtype, or cellstate of a specific cell type or subtype within a population of cells(e.g., immune evading tumor cells, immunotherapy resistant tumor cells,tumor infiltrating lymphocytes, macrophages). In certain embodiments,the expression of the immunotherapy resistant, T cell signature and/ormacrophage signature is dependent on epigenetic modification of thegenes or regulatory elements associated with the genes. Thus, in certainembodiments, use of signature genes includes epigenetic modificationsthat may be detected or modulated. For ease of discussion, whendiscussing gene expression, any of gene or genes, protein or proteins,or epigenetic element(s) may be substituted. As used herein, the terms“signature”, “expression profile”, or “expression program” may be usedinterchangeably. It is to be understood that also when referring toproteins (e.g. differentially expressed proteins), such may fall withinthe definition of “gene” signature. Levels of expression or activity maybe compared between different cells in order to characterize or identifyfor instance signatures specific for cell (sub)populations. Increased ordecreased expression or activity or prevalence of signature genes may becompared between different cells in order to characterize or identifyfor instance specific cell (sub)populations. The detection of asignature in single cells may be used to identify and quantitate forinstance specific cell (sub)populations. A signature may include a geneor genes, protein or proteins, or epigenetic element(s) whose expressionor occurrence is specific to a cell (sub)population, such thatexpression or occurrence is exclusive to the cell (sub)population. Agene signature as used herein, may thus refer to any set of up- and/ordown-regulated genes that are representative of a cell type or subtype.A gene signature as used herein, may also refer to any set of up- and/ordown-regulated genes between different cells or cell (sub)populationsderived from a gene-expression profile. For example, a gene signaturemay comprise a list of genes differentially expressed in a distinctionof interest.

The signature as defined herein (being it a gene signature, proteinsignature or other genetic or epigenetic signature) can be used toindicate the presence of a cell type, a subtype of the cell type, thestate of the microenvironment of a population of cells, a particularcell type population or subpopulation, and/or the overall status of theentire cell (sub)population. Furthermore, the signature may beindicative of cells within a population of cells in vivo. The signaturemay also be used to suggest for instance particular therapies, or tofollow up treatment, or to suggest ways to modulate immune systems. Thesignatures of the present invention may be discovered by analysis ofexpression profiles of single-cells within a population of cells fromisolated samples (e.g. tumor samples), thus allowing the discovery ofnovel cell subtypes or cell states that were previously invisible orunrecognized. The presence of subtypes or cell states may be determinedby subtype specific or cell state specific signatures. The presence ofthese specific cell (sub)types or cell states may be determined byapplying the signature genes to bulk sequencing data in a sample. Notbeing bound by a theory the signatures of the present invention may bemicroenvironment specific, such as their expression in a particularspatio-temporal context. Not being bound by a theory, signatures asdiscussed herein are specific to a particular pathological context. Notbeing bound by a theory, a combination of cell subtypes having aparticular signature may indicate an outcome. Not being bound by atheory, the signatures can be used to deconvolute the network of cellspresent in a particular pathological condition. Not being bound by atheory the presence of specific cells and cell subtypes are indicativeof a particular response to treatment, such as including increased ordecreased susceptibility to treatment. The signature may indicate thepresence of one particular cell type. In one embodiment, the novelsignatures are used to detect multiple cell states or hierarchies thatoccur in subpopulations of cells that are linked to particularpathological condition, or linked to a particular outcome or progressionof the disease, or linked to a particular response to treatment of thedisease (e.g. resistance to immunotherapy).

The signature according to certain embodiments of the present inventionmay comprise or consist of one or more genes, proteins and/or epigeneticelements, such as for instance 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more. Incertain embodiments, the signature may comprise or consist of two ormore genes, proteins and/or epigenetic elements, such as for instance 2,3, 4, 5, 6, 7, 8, 9, 10 or more. In certain embodiments, the signaturemay comprise or consist of three or more genes, proteins and/orepigenetic elements, such as for instance 3, 4, 5, 6, 7, 8, 9, 10 ormore. In certain embodiments, the signature may comprise or consist offour or more genes, proteins and/or epigenetic elements, such as forinstance 4, 5, 6, 7, 8, 9, 10 or more. In certain embodiments, thesignature may comprise or consist of five or more genes, proteins and/orepigenetic elements, such as for instance 5, 6, 7, 8, 9, 10 or more. Incertain embodiments, the signature may comprise or consist of six ormore genes, proteins and/or epigenetic elements, such as for instance 6,7, 8, 9, 10 or more. In certain embodiments, the signature may compriseor consist of seven or more genes, proteins and/or epigenetic elements,such as for instance 7, 8, 9, 10 or more. In certain embodiments, thesignature may comprise or consist of eight or more genes, proteinsand/or epigenetic elements, such as for instance 8, 9, 10 or more. Incertain embodiments, the signature may comprise or consist of nine ormore genes, proteins and/or epigenetic elements, such as for instance 9,10 or more. In certain embodiments, the signature may comprise orconsist of ten or more genes, proteins and/or epigenetic elements, suchas for instance 10, 11, 12, 13, 14, 15, or more. It is to be understoodthat a signature according to the invention may for instance alsoinclude genes or proteins as well as epigenetic elements combined.

In certain embodiments, a signature is characterized as being specificfor a particular cell or cell (sub)population if it is upregulated oronly present, detected or detectable in that particular cell or cell(sub)population, or alternatively is downregulated or only absent, orundetectable in that particular cell or cell (sub)population. In thiscontext, a signature consists of one or more differentially expressedgenes/proteins or differential epigenetic elements when comparingdifferent cells or cell (sub)populations, including comparing differentimmune cells or immune cell (sub)populations (e.g., T cells), as well ascomparing immune cells or immune cell (sub)populations with other immunecells or immune cell (sub)populations. It is to be understood that“differentially expressed” genes/proteins include genes/proteins whichare up- or down-regulated as well as genes/proteins which are turned onor off. When referring to up- or down-regulation, in certainembodiments, such up- or down-regulation is preferably at leasttwo-fold, such as two-fold, three-fold, four-fold, five-fold, or more,such as for instance at least ten-fold, at least 20-fold, at least30-fold, at least 40-fold, at least 50-fold, or more. Alternatively, orin addition, differential expression may be determined based on commonstatistical tests, as is known in the art.

As discussed herein, differentially expressed genes/proteins, ordifferential epigenetic elements may be differentially expressed on asingle cell level, or may be differentially expressed on a cellpopulation level. Preferably, the differentially expressedgenes/proteins or epigenetic elements as discussed herein, such asconstituting the gene signatures as discussed herein, when as to thecell population level, refer to genes that are differentially expressedin all or substantially all cells of the population (such as at least80%, preferably at least 90%, such as at least 95% of the individualcells). This allows one to define a particular subpopulation of cells.As referred to herein, a “subpopulation” of cells preferably refers to aparticular subset of cells of a particular cell type (e.g., resistant)which can be distinguished or are uniquely identifiable and set apartfrom other cells of this cell type. The cell subpopulation may bephenotypically characterized, and is preferably characterized by thesignature as discussed herein. A cell (sub)population as referred toherein may constitute of a (sub)population of cells of a particular celltype characterized by a specific cell state.

When referring to induction, or alternatively reducing or suppression ofa particular signature, preferable is meant induction or alternativelyreduction or suppression (or upregulation or downregulation) of at leastone gene/protein and/or epigenetic element of the signature, such as forinstance at least two, at least three, at least four, at least five, atleast six, or all genes/proteins and/or epigenetic elements of thesignature.

Various aspects and embodiments of the invention may involve analyzinggene signatures, protein signature, and/or other genetic or epigeneticsignature based on single cell analyses (e.g. single cell RNAsequencing) or alternatively based on cell population analyses, as isdefined herein elsewhere.

The invention further relates to various uses of the gene signatures,protein signature, and/or other genetic or epigenetic signature asdefined herein, as well as various uses of the immune cells or immunecell (sub)populations as defined herein. Particular advantageous usesinclude methods for identifying agents capable of inducing orsuppressing particular immune cell (sub)populations based on the genesignatures, protein signature, and/or other genetic or epigeneticsignature as defined herein. The invention further relates to agentscapable of inducing or suppressing particular immune cell(sub)populations based on the gene signatures, protein signature, and/orother genetic or epigenetic signature as defined herein, as well astheir use for modulating, such as inducing or repressing, a particulargene signature, protein signature, and/or other genetic or epigeneticsignature. In one embodiment, genes in one population of cells may beactivated or suppressed in order to affect the cells of anotherpopulation. In related aspects, modulating, such as inducing orrepressing, a particular gene signature, protein signature, and/or othergenetic or epigenetic signature may modify overall immune composition,such as immune cell composition, such as immune cell subpopulationcomposition or distribution, or functionality.

The signature genes of the present invention were discovered by analysisof expression profiles of single-cells within a population of tumorcells, thus allowing the discovery of novel cell subtypes that werepreviously invisible in a population of cells within a tumor. Thepresence of subtypes may be determined by subtype specific signaturegenes. The presence of these specific cell types may be determined byapplying the signature genes to bulk sequencing data in a patient. Notbeing bound by a theory, many cells that make up a microenvironment,whereby the cells communicate and affect each other in specific ways. Assuch, specific cell types within this microenvironment may expresssignature genes specific for this microenvironment. Not being bound by atheory the signature genes of the present invention may bemicroenvironment specific, such as their expression in a tumor. Thesignature genes may indicate the presence of one particular cell type.In one embodiment, the expression may indicate the presence ofimmunotherapy resistant cell types. Not being bound by a theory, acombination of cell subtypes in a subject may indicate an outcome (e.g.,resistant cells, cytotoxic T cells, Tregs).

In certain embodiments, the present invention provides for genesignature screening. The concept of signature screening was introducedby Stegmaier et al. (Gene expression-based high-throughput screening(GE-HTS) and application to leukemia differentiation. Nature Genet. 36,257-263 (2004)), who realized that if a gene-expression signature wasthe proxy for a phenotype of interest, it could be used to find smallmolecules that effect that phenotype without knowledge of a validateddrug target. The signature of the present may be used to screen fordrugs that reduce the signature in cancer cells or cell lines having aresistant signature as described herein. The signature may be used forGE-HTS. In certain embodiments, pharmacological screens may be used toidentify drugs that are selectively toxic to cancer cells having animmunotherapy resistant signature. In certain embodiments, drugsselectively toxic to cancer cells having an immunotherapy resistantsignature are used for treatment of a cancer patient. In certainembodiments, cells having an immunotherapy resistant signature asdescribed herein are treated with a plurality of drug candidates nottoxic to non-tumor cells and toxicity is assayed.

The Connectivity Map (cmap) is a collection of genome-widetranscriptional expression data from cultured human cells treated withbioactive small molecules and simple pattern-matching algorithms thattogether enable the discovery of functional connections between drugs,genes and diseases through the transitory feature of commongene-expression changes (see, Lamb et al., The Connectivity Map: UsingGene-Expression Signatures to Connect Small Molecules, Genes, andDisease. Science 29 Sep. 2006: Vol. 313, Issue 5795, pp. 1929-1935, DOI:10.1126/science.1132939; and Lamb, J., The Connectivity Map: a new toolfor biomedical research. Nature Reviews Cancer January 2007: Vol. 7, pp.54-60). Cmap can be used to screen for a signature in silico.

In one embodiment, the signature genes may be detected byimmunofluorescence, immunohistochemistry, fluorescence activated cellsorting (FACS), mass cytometry (CyTOF), Drop-seq, RNA-seq, scRNA-seq,InDrop, single cell qPCR, MERFISH (multiplex (in situ) RNA FISH) and/orby in situ hybridization. Other methods including absorbance assays andcolorimetric assays are known in the art and may be used herein.

All gene name symbols refer to the gene as commonly known in the art.The examples described herein refer to the human gene names and it is tobe understood that the present invention also encompasses genes fromother organisms (e.g., mouse genes). Gene symbols may be those referredto by the HUGO Gene Nomenclature Committee (HGNC) or National Center forBiotechnology Information (NCBI). Any reference to the gene symbol is areference made to the entire gene or variants of the gene. The signatureas described herein may encompass any of the genes described herein. Incertain embodiments, the gene signature includes surface expressed andsecreted proteins. Not being bound by a theory, surface proteins may betargeted for detection and isolation of cell types, or may be targetedtherapeutically to modulate an immune response.

As used herein, “modulating” or “to modulate” generally means eitherreducing or inhibiting the expression or activity of, or alternativelyincreasing the expression or activity of a target gene. In particular,“modulating” or “to modulate” can mean either reducing or inhibiting theactivity of, or alternatively increasing a (relevant or intended)biological activity of, a target or antigen as measured using a suitablein vitro, cellular or in vivo assay (which will usually depend on thetarget involved), by at least 5%, at least 10%, at least 25%, at least50%, at least 60%, at least 70%, at least 80%, at least 90%, or more,compared to activity of the target in the same assay under the sameconditions but without the presence of an agent. An “increase” or“decrease” refers to a statistically significant increase or decreaserespectively. For the avoidance of doubt, an increase or decrease willbe at least 10% relative to a reference, such as at least 10%, at least20%, at least 30%, at least 40%, at least 50%, a t least 60%, at least70%, at least 80%, at least 90%, at least 95%, at least 97%, at least98%, or more, up to and including at least 100% or more, in the case ofan increase, for example, at least 2-fold, at least 3-fold, at least4-fold, at least 5-fold, at least 6-fold, at least 7-fold, at least8-fold, at least 9-fold, at least 10-fold, at least 50-fold, at least100-fold, or more. “Modulating” can also involve effecting a change(which can either be an increase or a decrease) in affinity, avidity,specificity and/or selectivity of a target or antigen, such as areceptor and ligand. “Modulating” can also mean effecting a change withrespect to one or more biological or physiological mechanisms, effects,responses, functions, pathways or activities in which the target orantigen (or in which its substrate(s), ligand(s) or pathway(s) areinvolved, such as its signaling pathway or metabolic pathway and theirassociated biological or physiological effects) is involved. Again, aswill be clear to the skilled person, such an action as an agonist or anantagonist can be determined in any suitable manner and/or using anysuitable assay known or described herein (e.g., in vitro or cellularassay), depending on the target or antigen involved.

Modulating can, for example, also involve allosteric modulation of thetarget and/or reducing or inhibiting the binding of the target to one ofits substrates or ligands and/or competing with a natural ligand,substrate for binding to the target. Modulating can also involveactivating the target or the mechanism or pathway in which it isinvolved. Modulating can for example also involve effecting a change inrespect of the folding or confirmation of the target, or in respect ofthe ability of the target to fold, to change its conformation (forexample, upon binding of a ligand), to associate with other (sub)units,or to disassociate. Modulating can for example also involve effecting achange in the ability of the target to signal, phosphorylate,dephosphorylate, and the like.

Modulating Agents

As used herein, an “agent” can refer to a protein-binding agent thatpermits modulation of activity of proteins or disrupts interactions ofproteins and other biomolecules, such as but not limited to disruptingprotein-protein interaction, ligand-receptor interaction, orprotein-nucleic acid interaction. Agents can also refer to DNA targetingor RNA targeting agents. Agents may include a fragment, derivative andanalog of an active agent. The terms “fragment,” “derivative” and“analog” when referring to polypeptides as used herein refers topolypeptides which either retain substantially the same biologicalfunction or activity as such polypeptides. An analog includes aproprotein which can be activated by cleavage of the proprotein portionto produce an active mature polypeptide. Such agents include, but arenot limited to, antibodies (“antibodies” includes antigen-bindingportions of antibodies such as epitope- or antigen-binding peptides,paratopes, functional CDRs; recombinant antibodies; chimeric antibodies;humanized antibodies; nanobodies; tribodies; midibodies; orantigen-binding derivatives, analogs, variants, portions, or fragmentsthereof), protein-binding agents, nucleic acid molecules, smallmolecules, recombinant protein, peptides, aptamers, avimers andprotein-binding derivatives, portions or fragments thereof. An “agent”as used herein, may also refer to an agent that inhibits expression of agene, such as but not limited to a DNA targeting agent (e.g., CRISPRsystem, TALE, Zinc finger protein) or RNA targeting agent (e.g.,inhibitory nucleic acid molecules such as RNAi, miRNA, ribozyme).

The agents of the present invention may be modified, such that theyacquire advantageous properties for therapeutic use (e.g., stability andspecificity), but maintain their biological activity.

It is well known that the properties of certain proteins can bemodulated by attachment of polyethylene glycol (PEG) polymers, whichincreases the hydrodynamic volume of the protein and thereby slows itsclearance by kidney filtration. (See, e.g., Clark et al., J. Biol. Chem.271: 21969-21977 (1996)). Therefore, it is envisioned that certainagents can be PEGylated (e.g., on peptide residues) to provide enhancedtherapeutic benefits such as, for example, increased efficacy byextending half-life in vivo. In certain embodiments, PEGylation of theagents may be used to extend the serum half-life of the agents and allowfor particular agents to be capable of crossing the blood-brain barrier.

In regards to peptide PEGylation methods, reference is made to Lu etal., Int. J. Pept. Protein Res. 43: 127-38 (1994); Lu et al., Pept. Res.6: 140-6 (1993); Felix et al., Int. J. Pept. Protein Res. 46: 253-64(1995); Gaertner et al., Bioconjug. Chem. 7: 38-44 (1996); Tsutsumi etal., Thromb. Haemost. 77: 168-73 (1997); Francis et al., hit. J.Hematol. 68: 1-18 (1998); Roberts et al., J. Pharm. Sci. 87: 1440-45(1998); and Tan et al., Protein Expr. Purif. 12: 45-52 (1998).Polyethylene glycol or PEG is meant to encompass any of the forms of PEGthat have been used to derivatize other proteins, including, but notlimited to, mono-(C1-10) alkoxy or aryloxy-polyethylene glycol. SuitablePEG moieties include, for example, 40 kDa methoxy poly(ethylene glycol)propionaldehyde (Dow, Midland, Mich.); 60 kDa methoxy poly(ethyleneglycol) propionaldehyde (Dow, Midland, Mich.); 40 kDa methoxypoly(ethylene glycol) maleimido-propionamide (Dow, Midland, Mich.); 31kDa alpha-methyl-w-(3-oxopropoxy), polyoxyethylene (NOF Corporation,Tokyo); mPEG2-NHS-40k (Nektar); mPEG2-MAL-40k (Nektar), SUNBRIGHTGL2-400MA ((PEG)240 kDa) (NOF Corporation, Tokyo), SUNBRIGHT ME-200MA(PEG20 kDa) (NOF Corporation, Tokyo). The PEG groups are generallyattached to the peptide (e.g., neuromedin U receptor agonists orantagonists) via acylation or alkylation through a reactive group on thePEG moiety (for example, a maleimide, an aldehyde, amino, thiol, orester group) to a reactive group on the peptide (for example, analdehyde, amino, thiol, a maleimide, or ester group).

The PEG molecule(s) may be covalently attached to any Lys, Cys, orK(CO(CH2)2SH) residues at any position in a peptide. In certainembodiments, the neuromedin U receptor agonists described herein can bePEGylated directly to any amino acid at the N-terminus by way of theN-terminal amino group. A “linker arm” may be added to a peptide tofacilitate PEGylation. PEGylation at the thiol side-chain of cysteinehas been widely reported (see, e.g., Caliceti & Veronese, Adv. DrugDeliv. Rev. 55: 1261-77 (2003)). If there is no cysteine residue in thepeptide, a cysteine residue can be introduced through substitution or byadding a cysteine to the N-terminal amino acid.

Substitutions of amino acids may be used to modify an agent of thepresent invention. The phrase “substitution of amino acids” as usedherein encompasses substitution of amino acids that are the result ofboth conservative and non-conservative substitutions. Conservativesubstitutions are the replacement of an amino acid residue by anothersimilar residue in a polypeptide. Typical but not limiting conservativesubstitutions are the replacements, for one another, among the aliphaticamino acids Ala, Val, Leu and Ile; interchange of Ser and Thr containinghydroxy residues, interchange of the acidic residues Asp and Glu,interchange between the amide-containing residues Asn and Gln,interchange of the basic residues Lys and Arg, interchange of thearomatic residues Phe and Tyr, and interchange of the small-sized aminoacids Ala, Ser, Thr, Met, and Gly. Non-conservative substitutions arethe replacement, in a polypeptide, of an amino acid residue by anotherresidue which is not biologically similar. For example, the replacementof an amino acid residue with another residue that has a substantiallydifferent charge, a substantially different hydrophobicity, or asubstantially different spatial configuration.

The term “antibody” is used interchangeably with the term“immunoglobulin” herein, and includes intact antibodies, fragments ofantibodies, e.g., Fab, F(ab′)2 fragments, and intact antibodies andfragments that have been mutated either in their constant and/orvariable region (e.g., mutations to produce chimeric, partiallyhumanized, or fully humanized antibodies, as well as to produceantibodies with a desired trait, e.g., enhanced binding and/or reducedFcR binding). The term “fragment” refers to a part or portion of anantibody or antibody chain comprising fewer amino acid residues than anintact or complete antibody or antibody chain. Fragments can be obtainedvia chemical or enzymatic treatment of an intact or complete antibody orantibody chain. Fragments can also be obtained by recombinant means.Exemplary fragments include Fab, Fab′, F(ab′)2, Fabc, Fd, dAb, V_(HH)and scFv and/or Fv fragments.

As used herein, a preparation of antibody protein having less than about50% of non-antibody protein (also referred to herein as a “contaminatingprotein”), or of chemical precursors, is considered to be “substantiallyfree.” 40%, 30%, 20%, 10% and more preferably 5% (by dry weight), ofnon-antibody protein, or of chemical precursors is considered to besubstantially free. When the antibody protein or biologically activeportion thereof is recombinantly produced, it is also preferablysubstantially free of culture medium, i.e., culture medium representsless than about 30%, preferably less than about 20%, more preferablyless than about 10%, and most preferably less than about 5% of thevolume or mass of the protein preparation.

The term “antigen-binding fragment” refers to a polypeptide fragment ofan immunoglobulin or antibody that binds antigen or competes with intactantibody (i.e., with the intact antibody from which they were derived)for antigen binding (i.e., specific binding). As such these antibodiesor fragments thereof are included in the scope of the invention,provided that the antibody or fragment binds specifically to a targetmolecule.

It is intended that the term “antibody” encompass any Ig class or any Igsubclass (e.g. the IgG1, IgG2, IgG3, and IgG4 subclassess of IgG)obtained from any source (e.g., humans and non-human primates, and inrodents, lagomorphs, caprines, bovines, equines, ovines, etc.).

The term “Ig class” or “immunoglobulin class”, as used herein, refers tothe five classes of immunoglobulin that have been identified in humansand higher mammals, IgG, IgM, IgA, IgD, and IgE. The term “Ig subclass”refers to the two subclasses of IgM (H and L), three subclasses of IgA(IgA1, IgA2, and secretory IgA), and four subclasses of IgG (IgG1, IgG2,IgG3, and IgG4) that have been identified in humans and higher mammals.The antibodies can exist in monomeric or polymeric form; for example,IgM antibodies exist in pentameric form, and IgA antibodies exist inmonomeric, dimeric or multimeric form.

The term “IgG subclass” refers to the four subclasses of immunoglobulinclass IgG—IgG1, IgG2, IgG3, and IgG4 that have been identified in humansand higher mammals by the heavy chains of the immunoglobulins, V1-γ4,respectively. The term “single-chain immunoglobulin” or “single-chainantibody” (used interchangeably herein) refers to a protein having atwo-polypeptide chain structure consisting of a heavy and a light chain,said chains being stabilized, for example, by interchain peptidelinkers, which has the ability to specifically bind antigen. The term“domain” refers to a globular region of a heavy or light chainpolypeptide comprising peptide loops (e.g., comprising 3 to 4 peptideloops) stabilized, for example, by β pleated sheet and/or intrachaindisulfide bond. Domains are further referred to herein as “constant” or“variable”, based on the relative lack of sequence variation within thedomains of various class members in the case of a “constant” domain, orthe significant variation within the domains of various class members inthe case of a “variable” domain. Antibody or polypeptide “domains” areoften referred to interchangeably in the art as antibody or polypeptide“regions”. The “constant” domains of an antibody light chain arereferred to interchangeably as “light chain constant regions”, “lightchain constant domains”, “CL” regions or “CL” domains. The “constant”domains of an antibody heavy chain are referred to interchangeably as“heavy chain constant regions”, “heavy chain constant domains”, “CH”regions or “CH” domains). The “variable” domains of an antibody lightchain are referred to interchangeably as “light chain variable regions”,“light chain variable domains”, “VL” regions or “VL” domains). The“variable” domains of an antibody heavy chain are referred tointerchangeably as “heavy chain constant regions”, “heavy chain constantdomains”, “VH” regions or “VH” domains).

The term “region” can also refer to a part or portion of an antibodychain or antibody chain domain (e.g., a part or portion of a heavy orlight chain or a part or portion of a constant or variable domain, asdefined herein), as well as more discrete parts or portions of saidchains or domains. For example, light and heavy chains or light andheavy chain variable domains include “complementarity determiningregions” or “CDRs” interspersed among “framework regions” or “FRs”, asdefined herein.

The term “conformation” refers to the tertiary structure of a protein orpolypeptide (e.g., an antibody, antibody chain, domain or regionthereof). For example, the phrase “light (or heavy) chain conformation”refers to the tertiary structure of a light (or heavy) chain variableregion, and the phrase “antibody conformation” or “antibody fragmentconformation” refers to the tertiary structure of an antibody orfragment thereof.

The term “antibody-like protein scaffolds” or “engineered proteinscaffolds” broadly encompasses proteinaceous non-immunoglobulinspecific-binding agents, typically obtained by combinatorial engineering(such as site-directed random mutagenesis in combination with phagedisplay or other molecular selection techniques). Usually, suchscaffolds are derived from robust and small soluble monomeric proteins(such as Kunitz inhibitors or lipocalins) or from a stably foldedextra-membrane domain of a cell surface receptor (such as protein A,fibronectin or the ankyrin repeat).

Such scaffolds have been extensively reviewed in Binz et al.(Engineering novel binding proteins from nonimmunoglobulin domains. NatBiotechnol 2005, 23:1257-1268), Gebauer and Skerra (Engineered proteinscaffolds as next-generation antibody therapeutics. Curr Opin Chem Biol.2009, 13:245-55), Gill and Damle (Biopharmaceutical drug discovery usingnovel protein scaffolds. Curr Opin Biotechnol 2006, 17:653-658), Skerra(Engineered protein scaffolds for molecular recognition. J Mol Recognit2000, 13:167-187), and Skerra (Alternative non-antibody scaffolds formolecular recognition. Curr Opin Biotechnol 2007, 18:295-304), andinclude without limitation affibodies, based on the Z-domain ofstaphylococcal protein A, a three-helix bundle of 58 residues providingan interface on two of its alpha-helices (Nygren, Alternative bindingproteins: Affibody binding proteins developed from a small three-helixbundle scaffold. FEBS J 2008, 275:2668-2676); engineered Kunitz domainsbased on a small (ca. 58 residues) and robust, disulphide-crosslinkedserine protease inhibitor, typically of human origin (e.g. LACI-D1),which can be engineered for different protease specificities (Nixon andWood, Engineered protein inhibitors of proteases. Curr Opin Drug DiscovDev 2006, 9:261-268); monobodies or adnectins based on the 10thextracellular domain of human fibronectin III (10Fn3), which adopts anIg-like beta-sandwich fold (94 residues) with 2-3 exposed loops, butlacks the central disulphide bridge (Koide and Koide, Monobodies:antibody mimics based on the scaffold of the fibronectin type IIIdomain. Methods Mol Biol 2007, 352:95-109); anticalins derived from thelipocalins, a diverse family of eight-stranded beta-barrel proteins (ca.180 residues) that naturally form binding sites for small ligands bymeans of four structurally variable loops at the open end, which areabundant in humans, insects, and many other organisms (Skerra,Alternative binding proteins: Anticalins—harnessing the structuralplasticity of the lipocalin ligand pocket to engineer novel bindingactivities. FEBS J 2008, 275:2677-2683); DARPins, designed ankyrinrepeat domains (166 residues), which provide a rigid interface arisingfrom typically three repeated beta-turns (Stumpp et al., DARPins: a newgeneration of protein therapeutics. Drug Discov Today 2008, 13:695-701);avimers (multimerized LDLR-A module) (Silverman et al., Multivalentavimer proteins evolved by exon shuffling of a family of human receptordomains. Nat Biotechnol 2005, 23:1556-1561); and cysteine-rich knottinpeptides (Kolmar, Alternative binding proteins: biological activity andtherapeutic potential of cystine-knot miniproteins. FEBS J 2008,275:2684-2690).

“Specific binding” of an antibody means that the antibody exhibitsappreciable affinity for a particular antigen or epitope and, generally,does not exhibit significant cross reactivity. “Appreciable” bindingincludes binding with an affinity of at least 25 μM. Antibodies withaffinities greater than 1×10⁷ M⁻¹ (or a dissociation coefficient of 1 μMor less or a dissociation coefficient of 1 nm or less) typically bindwith correspondingly greater specificity. Values intermediate of thoseset forth herein are also intended to be within the scope of the presentinvention and antibodies of the invention bind with a range ofaffinities, for example, 100 nM or less, 75 nM or less, 50 nM or less,25 nM or less, for example 10 nM or less, 5 nM or less, 1 nM or less, orin embodiments 500 pM or less, 100 pM or less, 50 pM or less or 25 pM orless. An antibody that “does not exhibit significant crossreactivity” isone that will not appreciably bind to an entity other than its target(e.g., a different epitope or a different molecule). For example, anantibody that specifically binds to a target molecule will appreciablybind the target molecule but will not significantly react withnon-target molecules or peptides. An antibody specific for a particularepitope will, for example, not significantly crossreact with remoteepitopes on the same protein or peptide. Specific binding can bedetermined according to any art-recognized means for determining suchbinding. Preferably, specific binding is determined according toScatchard analysis and/or competitive binding assays.

As used herein, the term “affinity” refers to the strength of thebinding of a single antigen-combining site with an antigenicdeterminant. Affinity depends on the closeness of stereochemical fitbetween antibody combining sites and antigen determinants, on the sizeof the area of contact between them, on the distribution of charged andhydrophobic groups, etc. Antibody affinity can be measured byequilibrium dialysis or by the kinetic BIACORE™ method. The dissociationconstant, Kd, and the association constant, Ka, are quantitativemeasures of affinity.

As used herein, the term “monoclonal antibody” refers to an antibodyderived from a clonal population of antibody-producing cells (e.g., Blymphocytes or B cells) which is homogeneous in structure and antigenspecificity. The term “polyclonal antibody” refers to a plurality ofantibodies originating from different clonal populations ofantibody-producing cells which are heterogeneous in their structure andepitope specificity but which recognize a common antigen. Monoclonal andpolyclonal antibodies may exist within bodily fluids, as crudepreparations, or may be purified, as described herein.

The term “binding portion” of an antibody (or “antibody portion”)includes one or more complete domains, e.g., a pair of complete domains,as well as fragments of an antibody that retain the ability tospecifically bind to a target molecule. It has been shown that thebinding function of an antibody can be performed by fragments of afull-length antibody. Binding fragments are produced by recombinant DNAtechniques, or by enzymatic or chemical cleavage of intactimmunoglobulins. Binding fragments include Fab, Fab′, F(ab′)2, Fabc, Fd,dAb, Fv, single chains, single-chain antibodies, e.g., scFv, and singledomain antibodies.

“Humanized” forms of non-human (e.g., murine) antibodies are chimericantibodies that contain minimal sequence derived from non-humanimmunoglobulin. For the most part, humanized antibodies are humanimmunoglobulins (recipient antibody) in which residues from ahypervariable region of the recipient are replaced by residues from ahypervariable region of a non-human species (donor antibody) such asmouse, rat, rabbit or nonhuman primate having the desired specificity,affinity, and capacity. In some instances, FR residues of the humanimmunoglobulin are replaced by corresponding non-human residues.Furthermore, humanized antibodies may comprise residues that are notfound in the recipient antibody or in the donor antibody. Thesemodifications are made to further refine antibody performance. Ingeneral, the humanized antibody will comprise substantially all of atleast one, and typically two, variable domains, in which all orsubstantially all of the hypervariable regions correspond to those of anon-human immunoglobulin and all or substantially all of the FR regionsare those of a human immunoglobulin sequence. The humanized antibodyoptionally also will comprise at least a portion of an immunoglobulinconstant region (Fc), typically that of a human immunoglobulin.

Examples of portions of antibodies or epitope-binding proteinsencompassed by the present definition include: (i) the Fab fragment,having V_(L), C_(L), V_(H) and C_(H)1 domains; (ii) the Fab′ fragment,which is a Fab fragment having one or more cysteine residues at theC-terminus of the C_(H)1 domain; (iii) the Fd fragment having V_(H) andC_(H)1 domains; (iv) the Fd′ fragment having V_(H) and C_(H)1 domainsand one or more cysteine residues at the C-terminus of the CHI domain;(v) the Fv fragment having the V_(L) and V_(H) domains of a single armof an antibody; (vi) the dAb fragment (Ward et al., 341 Nature 544(1989)) which consists of a V_(H) domain or a V_(L) domain that bindsantigen; (vii) isolated CDR regions or isolated CDR regions presented ina functional framework; (viii) F(ab)₂ fragments which are bivalentfragments including two Fab′ fragments linked by a disulphide bridge atthe hinge region; (ix) single chain antibody molecules (e.g., singlechain Fv; scFv) (Bird et al., 242 Science 423 (1988); and Huston et al.,85 PNAS 5879 (1988)); (x) “diabodies” with two antigen binding sites,comprising a heavy chain variable domain (V_(H)) connected to a lightchain variable domain (V_(L)) in the same polypeptide chain (see, e.g.,EP 404,097; WO 93/11161; Hollinger et al., 90 PNAS 6444 (1993)); (xi)“linear antibodies” comprising a pair of tandem Fd segments(V_(H)-C_(h)1-V_(H)-C_(h)1) which, together with complementary lightchain polypeptides, form a pair of antigen binding regions (Zapata etal., Protein Eng. 8(10):1057-62 (1995); and U.S. Pat. No. 5,641,870).

As used herein, a “blocking” antibody or an antibody “antagonist” is onewhich inhibits or reduces biological activity of the antigen(s) itbinds. In certain embodiments, the blocking antibodies or antagonistantibodies or portions thereof described herein completely inhibit thebiological activity of the antigen(s).

Antibodies may act as agonists or antagonists of the recognizedpolypeptides. For example, the present invention includes antibodieswhich disrupt receptor/ligand interactions either partially or fully.The invention features both receptor-specific antibodies andligand-specific antibodies. The invention also featuresreceptor-specific antibodies which do not prevent ligand binding butprevent receptor activation. Receptor activation (i.e., signaling) maybe determined by techniques described herein or otherwise known in theart. For example, receptor activation can be determined by detecting thephosphorylation (e.g., tyrosine or serine/threonine) of the receptor orof one of its down-stream substrates by immunoprecipitation followed bywestern blot analysis. In specific embodiments, antibodies are providedthat inhibit ligand activity or receptor activity by at least 95%, atleast 90%, at least 85%, at least 80%, at least 75%, at least 70%, atleast 60%, or at least 50% of the activity in absence of the antibody.

The invention also features receptor-specific antibodies which bothprevent ligand binding and receptor activation as well as antibodiesthat recognize the receptor-ligand complex. Likewise, encompassed by theinvention are neutralizing antibodies which bind the ligand and preventbinding of the ligand to the receptor, as well as antibodies which bindthe ligand, thereby preventing receptor activation, but do not preventthe ligand from binding the receptor. Further included in the inventionare antibodies which activate the receptor. These antibodies may act asreceptor agonists, i.e., potentiate or activate either all or a subsetof the biological activities of the ligand-mediated receptor activation,for example, by inducing dimerization of the receptor. The antibodiesmay be specified as agonists, antagonists or inverse agonists forbiological activities comprising the specific biological activities ofthe peptides disclosed herein. The antibody agonists and antagonists canbe made using methods known in the art. See, e.g., PCT publication WO96/40281; U.S. Pat. No. 5,811,097; Deng et al., Blood 92(6):1981-1988(1998); Chen et al., Cancer Res. 58(16):3668-3678 (1998); Harrop et al.,J. Immunol. 161(4):1786-1794 (1998); Zhu et al., Cancer Res.58(15):3209-3214 (1998); Yoon et al., J. Immunol. 160(7):3170-3179(1998); Prat et al., J. Cell. Sci. III (Pt2):237-247 (1998); Pitard etal., J. Immunol. Methods 205(2):177-190 (1997); Liautard et al.,Cytokine 9(4):233-241 (1997); Carlson et al., J. Biol. Chem.272(17):11295-11301 (1997); Taryman et al., Neuron 14(4):755-762 (1995);Muller et al., Structure 6(9):1153-1167 (1998); Bartunek et al.,Cytokine 8(1):14-20 (1996).

The antibodies as defined for the present invention include derivativesthat are modified, i.e., by the covalent attachment of any type ofmolecule to the antibody such that covalent attachment does not preventthe antibody from generating an anti-idiotypic response. For example,but not by way of limitation, the antibody derivatives includeantibodies that have been modified, e.g., by glycosylation, acetylation,pegylation, phosphylation, amidation, derivatization by knownprotecting/blocking groups, proteolytic cleavage, linkage to a cellularligand or other protein, etc. Any of numerous chemical modifications maybe carried out by known techniques, including, but not limited tospecific chemical cleavage, acetylation, formylation, metabolicsynthesis of tunicamycin, etc. Additionally, the derivative may containone or more non-classical amino acids.

Simple binding assays can be used to screen for or detect agents thatbind to a target protein, or disrupt the interaction between proteins(e.g., a receptor and a ligand). Because certain targets of the presentinvention are transmembrane proteins, assays that use the soluble formsof these proteins rather than full-length protein can be used, in someembodiments. Soluble forms include, for example, those lacking thetransmembrane domain and/or those comprising the IgV domain or fragmentsthereof which retain their ability to bind their cognate bindingpartners. Further, agents that inhibit or enhance protein interactionsfor use in the compositions and methods described herein, can includerecombinant peptido-mimetics.

Detection methods useful in screening assays include antibody-basedmethods, detection of a reporter moiety, detection of cytokines asdescribed herein, and detection of a gene signature as described herein.

Another variation of assays to determine binding of a receptor proteinto a ligand protein is through the use of affinity biosensor methods.Such methods may be based on the piezoelectric effect, electrochemistry,or optical methods, such as ellipsometry, optical wave guidance, andsurface plasmon resonance (SPR).

The disclosure also encompasses nucleic acid molecules, in particularthose that inhibit a signature gene. Exemplary nucleic acid moleculesinclude aptamers, siRNA, artificial microRNA, interfering RNA or RNAi,dsRNA, ribozymes, antisense oligonucleotides, and DNA expressioncassettes encoding said nucleic acid molecules. Preferably, the nucleicacid molecule is an antisense oligonucleotide. Antisenseoligonucleotides (ASO) generally inhibit their target by binding targetmRNA and sterically blocking expression by obstructing the ribosome.ASOs can also inhibit their target by binding target mRNA thus forming aDNA-RNA hybrid that can be a substance for RNase H. Preferred ASOsinclude Locked Nucleic Acid (LNA), Peptide Nucleic Acid (PNA), andmorpholinos Preferably, the nucleic acid molecule is an RNAi molecule,i.e., RNA interference molecule. Preferred RNAi molecules include siRNA,shRNA, and artificial miRNA. The design and production of siRNAmolecules is well known to one of skill in the art (e.g., Hajeri P B,Singh S K. Drug Discov Today. 2009 14(17-18):851-8). The nucleic acidmolecule inhibitors may be chemically synthesized and provided directlyto cells of interest. The nucleic acid compound may be provided to acell as part of a gene delivery vehicle. Such a vehicle is preferably aliposome or a viral gene delivery vehicle.

Adoptive Cell Therapy

In certain embodiments, tumor cells are targeted by using Adoptive celltherapy. As used herein, “ACT”, “adoptive cell therapy” and “adoptivecell transfer” may be used interchangeably. Adoptive cell therapy (ACT)can refer to the transfer of cells, most commonly immune-derived cells,back into the same patient or into a new recipient host with the goal oftransferring the immunologic functionality and characteristics into thenew host. If possible, use of autologous cells helps the recipient byminimizing GVHD issues. The adoptive transfer of autologous tumorinfiltrating lymphocytes (TIL) (Besser et al., (2010) Clin. Cancer Res16 (9) 2646-55; Dudley et al., (2002) Science 298 (5594): 850-4; andDudley et al., (2005) Journal of Clinical Oncology 23 (10): 2346-57.) orgenetically re-directed peripheral blood mononuclear cells (Johnson etal., (2009) Blood 114 (3): 535-46; and Morgan et al., (2006) Science314(5796) 126-9) has been used to successfully treat patients withadvanced solid tumors, including melanoma and colorectal carcinoma, aswell as patients with CD19-expressing hematologic malignancies (Kalos etal., (2011) Science Translational Medicine 3 (95): 95ra73).

Aspects of the invention involve the adoptive transfer of immune systemcells, such as T cells, specific for selected antigens, such as tumorassociated antigens or tumor specific neoantigens (see Maus et al.,2014, Adoptive Immunotherapy for Cancer or Viruses, Annual Review ofImmunology, Vol. 32: 189-225; Rosenberg and Restifo, 2015, Adoptive celltransfer as personalized immunotherapy for human cancer, Science Vol.348 no. 6230 pp. 62-68; Restifo et al., 2015, Adoptive immunotherapy forcancer: harnessing the T cell response. Nat. Rev. Immunol. 12(4):269-281; and Jenson and Riddell, 2014, Design and implementation ofadoptive therapy with chimeric antigen receptor-modified T cells.Immunol Rev. 257(1): 127-144; and Rajasagi et al., 2014, Systematicidentification of personal tumor-specific neoantigens in chroniclymphocytic leukemia. Blood. 2014 Jul. 17; 124(3):453-62).

In certain embodiments, an antigen (such as a tumor antigen) to betargeted in adoptive cell therapy (such as particularly CAR or TCRT-cell therapy) of a disease (such as particularly of tumor or cancer)may be selected from a group consisting of: B cell maturation antigen(BCMA); PSA (prostate-specific antigen); prostate-specific membraneantigen (PSMA); PSCA (Prostate stem cell antigen); Tyrosine-proteinkinase transmembrane receptor ROR1; fibroblast activation protein (FAP);Tumor-associated glycoprotein 72 (TAG72); Carcinoembryonic antigen(CEA); Epithelial cell adhesion molecule (EPCAM); Mesothelin; HumanEpidermal growth factor Receptor 2 (ERBB2 (Her2/neu)); Prostase;Prostatic acid phosphatase (PAP); elongation factor 2 mutant (ELF2M);Insulin-like growth factor 1 receptor (IGF-1R); gplOO; BCR-ABL(breakpoint cluster region-Abelson); tyrosinase; New York esophagealsquamous cell carcinoma 1 (NY-ESO-1); κ-light chain, LAGE (L antigen);MAGE (melanoma antigen); Melanoma-associated antigen 1 (MAGE-A1); MAGEA3; MAGE A6; legumain; Human papillomavirus (HPV) E6; HPV E7; prostein;survivin; PCTA1 (Galectin 8); Melan-A/MART-1; Ras mutant; TRP-1(tyrosinase related protein 1, or gp75); Tyrosinase-related Protein 2(TRP2); TRP-2/INT2 (TRP-2/intron 2); RAGE (renal antigen); receptor foradvanced glycation end products 1 (RAGE1); Renal ubiquitous 1, 2 (RU1,RU2); intestinal carboxyl esterase (iCE); Heat shock protein 70-2(HSP70-2) mutant; thyroid stimulating hormone receptor (TSHR); CD123;CD171; CD19; CD20; CD22; CD26; CD30; CD33; CD44v7/8 (cluster ofdifferentiation 44, exons 7/8); CD53; CD92; CD100; CD148; CD150; CD200;CD261; CD262; CD362; CS-1 (CD2 subset 1, CRACC, SLAMF7, CD319, and19A24); C-type lectin-like molecule-1 (CLL-1); ganglioside GD3(aNeu5Ac(2-8)aNeu5Ac(2-3)bDGalp(1-4)bDGlcp(1-1)Cer); Tn antigen (Tn Ag);Fms-Like Tyrosine Kinase 3 (FLT3); CD38; CD138; CD44v6; B7H3 (CD276);KIT (CD117); Interleukin-13 receptor subunit alpha-2 (IL-13Ra2);Interleukin 11 receptor alpha (IL-11Ra); prostate stem cell antigen(PSCA); Protease Serine 21 (PRSS21); vascular endothelial growth factorreceptor 2 (VEGFR2); Lewis(Y) antigen; CD24; Platelet-derived growthfactor receptor beta (PDGFR-beta); stage-specific embryonic antigen-4(SSEA-4); Mucin 1, cell surface associated (MUC1); mucin 16 (MUC16);epidermal growth factor receptor (EGFR); epidermal growth factorreceptor variant III (EGFRvIII); neural cell adhesion molecule (NCAM);carbonic anhydrase IX (CAIX); Proteasome (Prosome, Macropain) Subunit,Beta Type, 9 (LMP2); ephrin type-A receptor 2 (EphA2); Ephrin B2;Fucosyl GM1; sialyl Lewis adhesion molecule (sLe); ganglioside GM3(aNeu5Ac(2-3)bDGalp(1-4)bDGlcp(1-1)Cer); TGS5; high molecularweight-melanoma-associated antigen (HMWMAA); o-acetyl-GD2 ganglioside(OAcGD2); Folate receptor alpha; Folate receptor beta; tumor endothelialmarker 1 (TEM1/CD248); tumor endothelial marker 7-related (TEM7R);claudin 6 (CLDN6); G protein-coupled receptor class C group 5, member D(GPRC5D); chromosome X open reading frame 61 (CXORF61); CD97; CD179a;anaplastic lymphoma kinase (ALK); Polysialic acid; placenta-specific 1(PLAC1); hexasaccharide portion of globoH glycoceramide (GloboH);mammary gland differentiation antigen (NY-BR-1); uroplakin 2 (UPK2);Hepatitis A virus cellular receptor 1 (HAVCR1); adrenoceptor beta 3(ADRB3); pannexin 3 (PANX3); G protein-coupled receptor 20 (GPR20);lymphocyte antigen 6 complex, locus K 9 (LY6K); Olfactory receptor 51E2(OR51E2); TCR Gamma Alternate Reading Frame Protein (TARP); Wilms tumorprotein (WT1); ETS translocation-variant gene 6, located on chromosome12p (ETV6-AML); sperm protein 17 (SPA17); X Antigen Family, Member 1A(XAGE1); angiopoietin-binding cell surface receptor 2 (Tie 2); CT(cancer/testis (antigen)); melanoma cancer testis antigen-1 (MAD-CT-1);melanoma cancer testis antigen-2 (MAD-CT-2); Fos-related antigen 1; p53;p53 mutant; human Telomerase reverse transcriptase (hTERT); sarcomatranslocation breakpoints; melanoma inhibitor of apoptosis (ML-IAP); ERG(transmembrane protease, serine 2 (TMPRSS2) ETS fusion gene); N-Acetylglucosaminyl-transferase V (NA17); paired box protein Pax-3 (PAX3);Androgen receptor; Cyclin B1; Cyclin D1; v-myc avian myelocytomatosisviral oncogene neuroblastoma derived homolog (MYCN); Ras Homolog FamilyMember C (RhoC); Cytochrome P450 1B1 (CYP1B1); CCCTC-Binding Factor(Zinc Finger Protein)-Like (BORIS); Squamous Cell Carcinoma AntigenRecognized By T Cells-1 or 3 (SART1, SART3); Paired box protein Pax-5(PAX5); proacrosin binding protein sp32 (OY-TES1); lymphocyte-specificprotein tyrosine kinase (LCK); A kinase anchor protein 4 (AKAP-4);synovial sarcoma, X breakpoint-1, -2, -3 or -4 (SSX1, SSX2, SSX3, SSX4);CD79a; CD79b; CD72; Leukocyte-associated immunoglobulin-like receptor 1(LAIR1); Fc fragment of IgA receptor (FCAR); Leukocyteimmunoglobulin-like receptor subfamily A member 2 (LILRA2); CD300molecule-like family member f (CD300LF); C-type lectin domain family 12member A (CLEC12A); bone marrow stromal cell antigen 2 (BST2); EGF-likemodule-containing mucin-like hormone receptor-like 2 (EMR2); lymphocyteantigen 75 (LY75); Glypican-3 (GPC3); Fc receptor-like 5 (FCRL5); mousedouble minute 2 homolog (MDM2); livin; alphafetoprotein (AFP);transmembrane activator and CAML Interactor (TACI); B-cell activatingfactor receptor (BAFF-R); V-Ki-ras2 Kirsten rat sarcoma viral oncogenehomolog (KRAS); immunoglobulin lambda-like polypeptide 1 (IGLL1); 707-AP(707 alanine proline); ART-4 (adenocarcinoma antigen recognized by T4cells); BAGE (B antigen; b-catenin/m, b-catenin/mutated); CAMEL(CTL-recognized antigen on melanoma); CAP1 (carcinoembryonic antigenpeptide 1); CASP-8 (caspase-8); CDC27m (cell-division cycle 27 mutated);CDK4/m (cycline-dependent kinase 4 mutated); Cyp-B (cyclophilin B); DAM(differentiation antigen melanoma); EGP-2 (epithelial glycoprotein 2);EGP-40 (epithelial glycoprotein 40); Erbb2, 3, 4 (erythroblasticleukemia viral oncogene homolog-2, -3, 4); FBP (folate bindingprotein);, fAchR (Fetal acetylcholine receptor); G250 (glycoprotein250); GAGE (G antigen); GnT-V (N-acetylglucosaminyltransferase V); HAGE(helicose antigen); ULA-A (human leukocyte antigen-A); HST2 (humansignet ring tumor 2); KIAA0205; KDR (kinase insert domain receptor);LDLR/FUT (low density lipid receptor/GDP L-fucose: b-D-galactosidase2-a-L fucosyltransferase); L1CAM (L1 cell adhesion molecule); MC1R(melanocortin 1 receptor); Myosin/m (myosin mutated); MUM-1, -2, -3(melanoma ubiquitous mutated 1, 2, 3); NA88-A (NA cDNA clone of patientM88); KG2D (Natural killer group 2, member D) ligands; oncofetal antigen(h5T4); p190 minor bcr-abl (protein of 190 KD bcr-abl); Pml/RARa(promyelocytic leukaemia/retinoic acid receptor a); PRAME(preferentially expressed antigen of melanoma); SAGE (sarcoma antigen);TEL/AML1 (translocation Ets-family leukemia/acute myeloid leukemia 1);TPI/m (triosephosphate isomerase mutated); and any combination thereof.

In certain embodiments, an antigen to be targeted in adoptive celltherapy (such as particularly CAR or TCR T-cell therapy) of a disease(such as particularly of tumor or cancer) is a tumor-specific antigen(TSA).

In certain embodiments, an antigen to be targeted in adoptive celltherapy (such as particularly CAR or TCR T-cell therapy) of a disease(such as particularly of tumor or cancer) is a neoantigen.

In certain embodiments, an antigen to be targeted in adoptive celltherapy (such as particularly CAR or TCR T-cell therapy) of a disease(such as particularly of tumor or cancer) is a tumor-associated antigen(TAA).

In certain embodiments, an antigen to be targeted in adoptive celltherapy (such as particularly CAR or TCR T-cell therapy) of a disease(such as particularly of tumor or cancer) is a universal tumor antigen.In certain preferred embodiments, the universal tumor antigen isselected from the group consisting of: a human telomerase reversetranscriptase (hTERT), survivin, mouse double minute 2 homolog (MDM2),cytochrome P450 1B 1 (CYP1B), HER2/neu, Wilms' tumor gene 1 (WT1),livin, alphafetoprotein (AFP), carcinoembryonic antigen (CEA), mucin 16(MUC16), MUC1, prostate-specific membrane antigen (PSMA), p53, cyclin(Dl), and any combinations thereof.

In certain embodiments, an antigen (such as a tumor antigen) to betargeted in adoptive cell therapy (such as particularly CAR or TCRT-cell therapy) of a disease (such as particularly of tumor or cancer)may be selected from a group consisting of: CD19, BCMA, CLL-1, MAGE A3,MAGE A6, HPV E6, HPV E7, WT1, CD22, CD171, ROR1, MUC16, and SSX2. Incertain preferred embodiments, the antigen may be CD19. For example,CD19 may be targeted in hematologic malignancies, such as in lymphomas,more particularly in B-cell lymphomas, such as without limitation indiffuse large B-cell lymphoma, primary mediastinal b-cell lymphoma,transformed follicular lymphoma, marginal zone lymphoma, mantle celllymphoma, acute lymphoblastic leukemia including adult and pediatricALL, non-Hodgkin lymphoma, indolent non-Hodgkin lymphoma, or chroniclymphocytic leukemia. For example, BCMA may be targeted in multiplemyeloma or plasma cell leukemia. For example, CLL1 may be targeted inacute myeloid leukemia. For example, MAGE A3, MAGE A6, SSX2, and/or KRASmay be targeted in solid tumors. For example, HPV E6 and/or HPV E7 maybe targeted in cervical cancer or head and neck cancer. For example, WT1may be targeted in acute myeloid leukemia (AML), myelodysplasticsyndromes (MDS), chronic myeloid leukemia (CIVIL), non-small cell lungcancer, breast, pancreatic, ovarian or colorectal cancers, ormesothelioma. For example, CD22 may be targeted in B cell malignancies,including non-Hodgkin lymphoma, diffuse large B-cell lymphoma, or acutelymphoblastic leukemia. For example, CD171 may be targeted inneuroblastoma, glioblastoma, or lung, pancreatic, or ovarian cancers.For example, ROR1 may be targeted in ROR1+ malignancies, includingnon-small cell lung cancer, triple negative breast cancer, pancreaticcancer, prostate cancer, ALL, chronic lymphocytic leukemia, or mantlecell lymphoma. For example, MUC16 may be targeted in MUC16ecto+epithelial ovarian, fallopian tube or primary peritoneal cancer.

Various strategies may for example be employed to genetically modify Tcells by altering the specificity of the T cell receptor (TCR) forexample by introducing new TCR α and β chains with selected peptidespecificity (see U.S. Pat. No. 8,697,854; PCT Patent Publications:WO2003020763, WO2004033685, WO2004044004, WO2005114215, WO2006000830,WO2008038002, WO2008039818, WO2004074322, WO2005113595, WO2006125962,WO2013166321, WO2013039889, WO2014018863, WO2014083173; U.S. Pat. No.8,088,379).

As an alternative to, or addition to, TCR modifications, chimericantigen receptors (CARs) may be used in order to generateimmunoresponsive cells, such as T cells, specific for selected targets,such as malignant cells, with a wide variety of receptor chimeraconstructs having been described (see U.S. Pat. Nos. 5,843,728;5,851,828; 5,912,170; 6,004,811; 6,284,240; 6,392,013; 6,410,014;6,753,162; 8,211,422; and, PCT Publication WO9215322).

In general, CARs are comprised of an extracellular domain, atransmembrane domain, and an intracellular domain, wherein theextracellular domain comprises an antigen-binding domain that isspecific for a predetermined target. While the antigen-binding domain ofa CAR is often an antibody or antibody fragment (e.g., a single chainvariable fragment, scFv), the binding domain is not particularly limitedso long as it results in specific recognition of a target. For example,in some embodiments, the antigen-binding domain may comprise a receptor,such that the CAR is capable of binding to the ligand of the receptor.Alternatively, the antigen-binding domain may comprise a ligand, suchthat the CAR is capable of binding the endogenous receptor of thatligand.

The antigen-binding domain of a CAR is generally separated from thetransmembrane domain by a hinge or spacer. The spacer is also notparticularly limited, and it is designed to provide the CAR withflexibility. For example, a spacer domain may comprise a portion of ahuman Fc domain, including a portion of the CH3 domain, or the hingeregion of any immunoglobulin, such as IgA, IgD, IgE, IgG, or IgM, orvariants thereof. Furthermore, the hinge region may be modified so as toprevent off-target binding by FcRs or other potential interferingobjects. For example, the hinge may comprise an IgG4 Fc domain with orwithout a S228P, L235E, and/or N297Q mutation (according to Kabatnumbering) in order to decrease binding to FcRs. Additionalspacers/hinges include, but are not limited to, CD4, CD8, and CD28 hingeregions.

The transmembrane domain of a CAR may be derived either from a naturalor from a synthetic source. Where the source is natural, the domain maybe derived from any membrane bound or transmembrane protein.Transmembrane regions of particular use in this disclosure may bederived from CD8, CD28, CD3, CD45, CD4, CD5, CDS, CD9, CD 16, CD22,CD33, CD37, CD64, CD80, CD86, CD 134, CD137, CD 154, TCR. Alternatively,the transmembrane domain may be synthetic, in which case it willcomprise predominantly hydrophobic residues such as leucine and valine.Preferably a triplet of phenylalanine, tryptophan and valine will befound at each end of a synthetic transmembrane domain. Optionally, ashort oligo- or polypeptide linker, preferably between 2 and 10 aminoacids in length may form the linkage between the transmembrane domainand the cytoplasmic signaling domain of the CAR. A glycine-serinedoublet provides a particularly suitable linker.

Alternative CAR constructs may be characterized as belonging tosuccessive generations. First-generation CARs typically consist of asingle-chain variable fragment of an antibody specific for an antigen,for example comprising a VL linked to a VH of a specific antibody,linked by a flexible linker, for example by a CD8α hinge domain and aCD8α transmembrane domain, to the transmembrane and intracellularsignaling domains of either CD3ζ or FcRγ (scFv-CD3ζ or scFv-FcRγ; seeU.S. Pat. Nos. 7,741,465; 5,912,172; 5,906,936). Second-generation CARsincorporate the intracellular domains of one or more costimulatorymolecules, such as CD28, OX40 (CD134), or 4-1BB (CD137) within theendodomain (for example scFv-CD28/OX40/4-1BB-CD3ζ; see U.S. Pat. Nos.8,911,993; 8,916,381; 8,975,071; 9,101,584; 9,102,760; 9,102,761).Third-generation CARs include a combination of costimulatoryendodomains, such a CD3ζ-chain, CD97, GDI 1a-CD18, CD2, ICOS, CD27,CD154, CDS, OX40, 4-1BB, CD2, CD7, LIGHT, LFA-1, NKG2C, B7-H3, CD30,CD40, PD-1, or CD28 signaling domains (for example scFv-CD28-4-1BB-CD3ζor scFv-CD28-OX40-CD3ζ; see U.S. Pat. Nos. 8,906,682; 8,399,645;5,686,281; PCT Publication No. WO2014134165; PCT Publication No.WO2012079000). In certain embodiments, the primary signaling domaincomprises a functional signaling domain of a protein selected from thegroup consisting of CD3 zeta, CD3 gamma, CD3 delta, CD3 epsilon, commonFcR gamma (FCERIG), FcR beta (Fc Epsilon Rib), CD79a, CD79b, Fc gammaRIIa, DAP10, and DAP12. In certain preferred embodiments, the primarysignaling domain comprises a functional signaling domain of CD3ζ orFcRγ. In certain embodiments, the one or more costimulatory signalingdomains comprise a functional signaling domain of a protein selected,each independently, from the group consisting of: CD27, CD28, 4-1BB(CD137), OX40, CD30, CD40, PD-1, ICOS, lymphocyte function-associatedantigen-1 (LFA-1), CD2, CD7, LIGHT, NKG2C, B7-H3, a ligand thatspecifically binds with CD83, CDS, ICAM-1, GITR, BAFFR, HVEM (LIGHTR),SLAMF7, NKp80 (KLRF1), CD160, CD19, CD4, CD8 alpha, CD8 beta, IL2R beta,IL2R gamma, IL7R alpha, ITGA4, VLA1, CD49a, ITGA4, IA4, CD49D, ITGA6,VLA-6, CD49f, ITGAD, CD11d, ITGAE, CD103, ITGAL, CD11a, LFA-1, ITGAM,CD11b, ITGAX, CD11c, ITGB1, CD29, ITGB2, CD18, ITGB7, TNFR2,TRANCE/RANKL, DNAM1 (CD226), SLAMF4 (CD244, 2B4), CD84, CD96 (Tactile),CEACAM1, CRTAM, Ly9 (CD229), CD160 (BY55), PSGL1, CD100 (SEMA4D), CD69,SLAMF6 (NTB-A, Ly108), SLAM (SLAMF1, CD150, IPO-3), BLAME (SLAMF8),SELPLG (CD162), LTBR, LAT, GADS, SLP-76, PAG/Cbp, NKp44, NKp30, NKp46,and NKG2D. In certain embodiments, the one or more costimulatorysignaling domains comprise a functional signaling domain of a proteinselected, each independently, from the group consisting of: 4-1BB, CD27,and CD28. In certain embodiments, a chimeric antigen receptor may havethe design as described in U.S. Pat. No. 7,446,190, comprising anintracellular domain of CD3ζ chain (such as amino acid residues 52-163of the human CD3 zeta chain, as shown in SEQ ID NO: 14 of U.S. Pat. No.7,446,190), a signaling region from CD28 and an antigen-binding element(or portion or domain; such as scFv). The CD28 portion, when between thezeta chain portion and the antigen-binding element, may suitably includethe transmembrane and signaling domains of CD28 (such as amino acidresidues 114-220 of SEQ ID NO: 10, full sequence shown in SEQ ID NO: 6of U.S. Pat. No. 7,446,190; these can include the following portion ofCD28 as set forth in Genbank identifier NM_006139 (sequence version 1, 2or 3): IEVMYPPPYLDNEKSNGTIIHVKGKHLCPSPLFPGPSKPFWVLVVVGGVLACYSLLVTVAFIIFWVRSKRSRLLHSDYMNMTPRRPGPTRKHYQPYAPPRDFA AYRS) SEQ IDNo: 1). Alternatively, when the zeta sequence lies between the CD28sequence and the antigen-binding element, intracellular domain of CD28can be used alone (such as amino sequence set forth in SEQ ID NO: 9 ofU.S. Pat. No. 7,446,190). Hence, certain embodiments employ a CARcomprising (a) a zeta chain portion comprising the intracellular domainof human CD3ζ chain, (b) a costimulatory signaling region, and (c) anantigen-binding element (or portion or domain), wherein thecostimulatory signaling region comprises the amino acid sequence encodedby SEQ ID NO: 6 of U.S. Pat. No. 7,446,190.

Alternatively, costimulation may be orchestrated by expressing CARs inantigen-specific T cells, chosen so as to be activated and expandedfollowing engagement of their native αβTCR, for example by antigen onprofessional antigen-presenting cells, with attendant costimulation. Inaddition, additional engineered receptors may be provided on theimmunoresponsive cells, for example to improve targeting of a T-cellattack and/or minimize side effects

By means of an example and without limitation, Kochenderfer et al.,(2009) J Immunother. 32 (7): 689-702 described anti-CD19 chimericantigen receptors (CAR). FMC63-28Z CAR contained a single chain variableregion moiety (scFv) recognizing CD19 derived from the FMC63 mousehybridoma (described in Nicholson et al., (1997) Molecular Immunology34: 1157-1165), a portion of the human CD28 molecule, and theintracellular component of the human TCR-molecule. FMC63-CD828BBZ CARcontained the FMC63 scFv, the hinge and transmembrane regions of the CD8molecule, the cytoplasmic portions of CD28 and 4-1BB, and thecytoplasmic component of the TCR-molecule. The exact sequence of theCD28 molecule included in the FMC63-28Z CAR corresponded to Genbankidentifier NM_006139; the sequence included all amino acids startingwith the amino acid sequence IEVMYPPPY and continuing all the way to thecarboxy-terminus of the protein. To encode the anti-CD19 scFv componentof the vector, the authors designed a DNA sequence which was based on aportion of a previously published CAR (Cooper et al., (2003) Blood 101:1637-1644). This sequence encoded the following components in frame fromthe 5′ end to the 3′ end: an XhoI site, the human granulocyte-macrophagecolony-stimulating factor (GM-CSF) receptor α-chain signal sequence, theFMC63 light chain variable region (as in Nicholson et al., supra), alinker peptide (as in Cooper et al., supra), the FMC63 heavy chainvariable region (as in Nicholson et al., supra), and a NotI site. Aplasmid encoding this sequence was digested with XhoI and NotI. To formthe MSGV-FMC63-28Z retroviral vector, the XhoI and NotI-digestedfragment encoding the FMC63 scFv was ligated into a second XhoI andNotI-digested fragment that encoded the MSGV retroviral backbone (as inHughes et al., (2005) Human Gene Therapy 16: 457-472) as well as part ofthe extracellular portion of human CD28, the entire transmembrane andcytoplasmic portion of human CD28, and the cytoplasmic portion of thehuman TCR-molecule (as in Maher et al., 2002) Nature Biotechnology 20:70-75). The FMC63-28Z CAR is included in the KTE-C19 (axicabtageneciloleucel) anti-CD19 CAR-T therapy product in development by KitePharma, Inc. for the treatment of inter alia patients withrelapsed/refractory aggressive B-cell non-Hodgkin lymphoma (NHL).Accordingly, in certain embodiments, cells intended for adoptive celltherapies, more particularly immunoresponsive cells such as T cells, mayexpress the FMC63-28Z CAR as described by Kochenderfer et al. (supra).Hence, in certain embodiments, cells intended for adoptive celltherapies, more particularly immunoresponsive cells such as T cells, maycomprise a CAR comprising an extracellular antigen-binding element (orportion or domain; such as scFv) that specifically binds to an antigen,an intracellular signaling domain comprising an intracellular domain ofa CD3ζ chain, and a costimulatory signaling region comprising asignaling domain of CD28. Preferably, the CD28 amino acid sequence is asset forth in Genbank identifier NM 006139 (sequence version 1, 2 or 3)starting with the amino acid sequence IEVMYPPPY (SEQ ID No: 2) andcontinuing all the way to the carboxy-terminus of the protein. Thesequence is reproduced herein:IEVMYPPPYLDNEKSNGTIIHVKGKHLCPSPLFPGPSKPFWVLVVVGGVLACYSLLVTVAFIIFWVRSKRSRLLHSDYMNMTPRRPGPTRKHYQPYAPPRDFAAYRS (SEQ. ID. No: 3).Preferably, the antigen is CD19, more preferably the antigen-bindingelement is an anti-CD19 scFv, even more preferably the anti-CD19 scFv asdescribed by Kochenderfer et al. (supra).

Additional anti-CD19 CARs are further described in WO2015187528. Moreparticularly Example 1 and Table 1 of WO2015187528, incorporated byreference herein, demonstrate the generation of anti-CD19 CARs based ona fully human anti-CD19 monoclonal antibody (47G4, as described inUS20100104509) and murine anti-CD19 monoclonal antibody (as described inNicholson et al. and explained above). Various combinations of a signalsequence (human CD8-alpha or GM-CSF receptor), extracellular andtransmembrane regions (human CD8-alpha) and intracellular T-cellsignalling domains (CD28-CD3ζ; 4-1BB-CD3ζ; CD27-CD3ζ; CD28-CD27-CD3ζ,4-1BB-CD27-CD3ζ; CD27-4-1BB-CD3ζ; CD28-CD27-FcεRI gamma chain; orCD28-FcεRI gamma chain) were disclosed. Hence, in certain embodiments,cells intended for adoptive cell therapies, more particularlyimmunoresponsive cells such as T cells, may comprise a CAR comprising anextracellular antigen-binding element that specifically binds to anantigen, an extracellular and transmembrane region as set forth in Table1 of WO2015187528 and an intracellular T-cell signalling domain as setforth in Table 1 of WO2015187528. Preferably, the antigen is CD19, morepreferably the antigen-binding element is an anti-CD19 scFv, even morepreferably the mouse or human anti-CD19 scFv as described in Example 1of WO2015187528. In certain embodiments, the CAR comprises, consistsessentially of or consists of an amino acid sequence of SEQ ID NO: 1,SEQ ID NO: 2, SEQ ID NO: 3, SEQ ID NO: 4, SEQ ID NO: 5, SEQ ID NO: 6,SEQ ID NO: 7, SEQ ID NO: 8, SEQ ID NO: 9, SEQ ID NO: 10, SEQ ID NO: 11,SEQ ID NO: 12, or SEQ ID NO: 13 as set forth in Table 1 of WO2015187528.

In certain embodiments, the immune cell may, in addition to a CAR orexogenous TCR as described herein, further comprise a chimericinhibitory receptor (inhibitory CAR) that specifically binds to a secondtarget antigen and is capable of inducing an inhibitory orimmunosuppressive or repressive signal to the cell upon recognition ofthe second target antigen. In certain embodiments, the chimericinhibitory receptor comprises an extracellular antigen-binding element(or portion or domain) configured to specifically bind to a targetantigen, a transmembrane domain, and an intracellular immunosuppressiveor repressive signaling domain. In certain embodiments, the secondtarget antigen is an antigen that is not expressed on the surface of acancer cell or infected cell or the expression of which is downregulatedon a cancer cell or an infected cell. In certain embodiments, the secondtarget antigen is an MHC-class I molecule. In certain embodiments, theintracellular signaling domain comprises a functional signaling portionof an immune checkpoint molecule, such as for example PD-1 or CTLA4.Advantageously, the inclusion of such inhibitory CAR reduces the chanceof the engineered immune cells attacking non-target (e.g., non-cancer)tissues.

Alternatively, T-cells expressing CARs may be further modified to reduceor eliminate expression of endogenous TCRs in order to reduce off-targeteffects. Reduction or elimination of endogenous TCRs can reduceoff-target effects and increase the effectiveness of the T cells (U.S.Pat. No. 9,181,527). T cells stably lacking expression of a functionalTCR may be produced using a variety of approaches. T cells internalize,sort, and degrade the entire T cell receptor as a complex, with ahalf-life of about 10 hours in resting T cells and 3 hours in stimulatedT cells (von Essen, M. et al. 2004. J. Immunol. 173:384-393). Properfunctioning of the TCR complex requires the proper stoichiometric ratioof the proteins that compose the TCR complex. TCR function also requirestwo functioning TCR zeta proteins with ITAM motifs. The activation ofthe TCR upon engagement of its MHC-peptide ligand requires theengagement of several TCRs on the same T cell, which all must signalproperly. Thus, if a TCR complex is destabilized with proteins that donot associate properly or cannot signal optimally, the T cell will notbecome activated sufficiently to begin a cellular response.

Accordingly, in some embodiments, TCR expression may eliminated usingRNA interference (e.g., shRNA, siRNA, miRNA, etc.), CRISPR, or othermethods that target the nucleic acids encoding specific TCRs (e.g.,TCR-α and TCR-β) and/or CD3 chains in primary T cells. By blockingexpression of one or more of these proteins, the T cell will no longerproduce one or more of the key components of the TCR complex, therebydestabilizing the TCR complex and preventing cell surface expression ofa functional TCR.

In some instances, CAR may also comprise a switch mechanism forcontrolling expression and/or activation of the CAR. For example, a CARmay comprise an extracellular, transmembrane, and intracellular domain,in which the extracellular domain comprises a target-specific bindingelement that comprises a label, binding domain, or tag that is specificfor a molecule other than the target antigen that is expressed on or bya target cell. In such embodiments, the specificity of the CAR isprovided by a second construct that comprises a target antigen bindingdomain (e.g., an scFv or a bispecific antibody that is specific for boththe target antigen and the label or tag on the CAR) and a domain that isrecognized by or binds to the label, binding domain, or tag on the CAR.See, e.g., WO 2013/044225, WO 2016/000304, WO 2015/057834, WO2015/057852, WO 2016/070061, U.S. Pat. No. 9,233,125, US 2016/0129109.In this way, a T-cell that expresses the CAR can be administered to asubject, but the CAR cannot bind its target antigen until the secondcomposition comprising an antigen-specific binding domain isadministered.

Alternative switch mechanisms include CARs that require multimerizationin order to activate their signaling function (see, e.g., US2015/0368342, US 2016/0175359, US 2015/0368360) and/or an exogenoussignal, such as a small molecule drug (US 2016/0166613, Yung et al.,Science, 2015), in order to elicit a T-cell response. Some CARs may alsocomprise a “suicide switch” to induce cell death of the CAR T-cellsfollowing treatment (Buddee et al., PLoS One, 2013) or to downregulateexpression of the CAR following binding to the target antigen (WO2016/011210).

Alternative techniques may be used to transform target immunoresponsivecells, such as protoplast fusion, lipofection, transfection orelectroporation. A wide variety of vectors may be used, such asretroviral vectors, lentiviral vectors, adenoviral vectors,adeno-associated viral vectors, plasmids or transposons, such as aSleeping Beauty transposon (see U.S. Pat. Nos. 6,489,458; 7,148,203;7,160,682; 7,985,739; 8,227,432), may be used to introduce CARs, forexample using 2nd generation antigen-specific CARs signaling throughCD3ζ and either CD28 or CD137. Viral vectors may for example includevectors based on HIV, SV40, EBV, HSV or BPV.

Cells that are targeted for transformation may for example include Tcells, Natural Killer (NK) cells, cytotoxic T lymphocytes (CTL),regulatory T cells, human embryonic stem cells, tumor-infiltratinglymphocytes (TIL) or a pluripotent stem cell from which lymphoid cellsmay be differentiated. T cells expressing a desired CAR may for examplebe selected through co-culture with γ-irradiated activating andpropagating cells (AaPC), which co-express the cancer antigen andco-stimulatory molecules. The engineered CAR T-cells may be expanded,for example by co-culture on AaPC in presence of soluble factors, suchas IL-2 and IL-21. This expansion may for example be carried out so asto provide memory CAR+ T cells (which may for example be assayed bynon-enzymatic digital array and/or multi-panel flow cytometry). In thisway, CAR T cells may be provided that have specific cytotoxic activityagainst antigen-bearing tumors (optionally in conjunction withproduction of desired chemokines such as interferon-γ). CAR T cells ofthis kind may for example be used in animal models, for example to treattumor xenografts.

In certain embodiments, ACT includes co-transferring CD4+Th1 cells andCD8+ CTLs to induce a synergistic antitumour response (see, e.g., Li etal., Adoptive cell therapy with CD4+T helper 1 cells and CD8+ cytotoxicT cells enhances complete rejection of an established tumour, leading togeneration of endogenous memory responses to non-targeted tumourepitopes. Clin Transl Immunology. 2017 October; 6(10): e160).

In certain embodiments, Th17 cells are transferred to a subject in needthereof. Th17 cells have been reported to directly eradicate melanomatumors in mice to a greater extent than Th1 cells (Muranski P, et al.,Tumor-specific Th17-polarized cells eradicate large establishedmelanoma. Blood. 2008 Jul. 15; 112(2):362-73; and Martin-Orozco N, etal., T helper 17 cells promote cytotoxic T cell activation in tumorimmunity. Immunity. 2009 Nov. 20; 31(5):787-98). Those studies involvedan adoptive T cell transfer (ACT) therapy approach, which takesadvantage of CD4⁺ T cells that express a TCR recognizing tyrosinasetumor antigen. Exploitation of the TCR leads to rapid expansion of Th17populations to large numbers ex vivo for reinfusion into the autologoustumor-bearing hosts.

In certain embodiments, ACT may include autologous iPSC-based vaccines,such as irradiated iPSCs in autologous anti-tumor vaccines (see e.g.,Kooreman, Nigel G. et al., Autologous iPSC-Based Vaccines ElicitAnti-tumor Responses In Vivo, Cell Stem Cell 22, 1-13, 2018,doi.org/10.1016/j.stem.2018.01.016).

Unlike T-cell receptors (TCRs) that are MHC restricted, CARs canpotentially bind any cell surface-expressed antigen and can thus be moreuniversally used to treat patients (see Irving et al., EngineeringChimeric Antigen Receptor T-Cells for Racing in Solid Tumors: Don'tForget the Fuel, Front. Immunol., 3 Apr. 2017,doi.org/10.3389/fimmu.2017.00267). In certain embodiments, in theabsence of endogenous T-cell infiltrate (e.g., due to aberrant antigenprocessing and presentation), which precludes the use of TIL therapy andimmune checkpoint blockade, the transfer of CAR T-cells may be used totreat patients (see, e.g., Hinrichs C S, Rosenberg S A. Exploiting thecurative potential of adoptive T-cell therapy for cancer. Immunol Rev(2014) 257(1):56-71. doi:10.1111/imr.12132).

Approaches such as the foregoing may be adapted to provide methods oftreating and/or increasing survival of a subject having a disease, suchas a neoplasia, for example by administering an effective amount of animmunoresponsive cell comprising an antigen recognizing receptor thatbinds a selected antigen, wherein the binding activates theimmunoresponsive cell, thereby treating or preventing the disease (suchas a neoplasia, a pathogen infection, an autoimmune disorder, or anallogeneic transplant reaction).

In certain embodiments, the treatment can be administered afterlymphodepleting pretreatment in the form of chemotherapy (typically acombination of cyclophosphamide and fludarabine) or radiation therapy.Initial studies in ACT had short lived responses and the transferredcells did not persist in vivo for very long (Houot et al., T-cell-basedimmunotherapy: adoptive cell transfer and checkpoint inhibition. CancerImmunol Res (2015) 3(10):1115-22; and Kamta et al., Advancing CancerTherapy with Present and Emerging Immuno-Oncology Approaches. Front.Oncol. (2017) 7:64). Immune suppressor cells like Tregs and MDSCs mayattenuate the activity of transferred cells by outcompeting them for thenecessary cytokines. Not being bound by a theory lymphodepletingpretreatment may eliminate the suppressor cells allowing the TILs topersist.

In one embodiment, the treatment can be administrated into patientsundergoing an immunosuppressive treatment. The cells or population ofcells, may be made resistant to at least one immunosuppressive agent dueto the inactivation of a gene encoding a receptor for suchimmunosuppressive agent. Not being bound by a theory, theimmunosuppressive treatment should help the selection and expansion ofthe immunoresponsive or T cells according to the invention within thepatient.

In certain embodiments, the treatment can be administered before primarytreatment (e.g., surgery or radiation therapy) to shrink a tumor beforethe primary treatment. In another embodiment, the treatment can beadministered after primary treatment to remove any remaining cancercells.

In certain embodiments, immunometabolic barriers can be targetedtherapeutically prior to and/or during ACT to enhance responses to ACTor CAR T-cell therapy and to support endogenous immunity (see, e.g.,Irving et al., Engineering Chimeric Antigen Receptor T-Cells for Racingin Solid Tumors: Don't Forget the Fuel, Front. Immunol., 3 Apr. 2017,doi.org/10.3389/fimmu.2017.00267).

The administration of cells or population of cells, such as immunesystem cells or cell populations, such as more particularlyimmunoresponsive cells or cell populations, as disclosed herein may becarried out in any convenient manner, including by aerosol inhalation,injection, ingestion, transfusion, implantation or transplantation. Thecells or population of cells may be administered to a patientsubcutaneously, intradermally, intratumorally, intranodally,intramedullary, intramuscularly, intrathecally, by intravenous orintralymphatic injection, or intraperitoneally. In some embodiments, thedisclosed CARs may be delivered or administered into a cavity formed bythe resection of tumor tissue (i.e. intracavity delivery) or directlyinto a tumor prior to resection (i.e. intratumoral delivery). In oneembodiment, the cell compositions of the present invention arepreferably administered by intravenous injection.

The administration of the cells or population of cells can consist ofthe administration of 10⁴-10⁹ cells per kg body weight, preferably 10⁵to 10⁶ cells/kg body weight including all integer values of cell numberswithin those ranges. Dosing in CAR T cell therapies may for exampleinvolve administration of from 10⁶ to 10⁹ cells/kg, with or without acourse of lymphodepletion, for example with cyclophosphamide. The cellsor population of cells can be administrated in one or more doses. Inanother embodiment, the effective amount of cells are administrated as asingle dose. In another embodiment, the effective amount of cells areadministrated as more than one dose over a period time. Timing ofadministration is within the judgment of managing physician and dependson the clinical condition of the patient. The cells or population ofcells may be obtained from any source, such as a blood bank or a donor.While individual needs vary, determination of optimal ranges ofeffective amounts of a given cell type for a particular disease orconditions are within the skill of one in the art. An effective amountmeans an amount which provides a therapeutic or prophylactic benefit.The dosage administrated will be dependent upon the age, health andweight of the recipient, kind of concurrent treatment, if any, frequencyof treatment and the nature of the effect desired.

In another embodiment, the effective amount of cells or compositioncomprising those cells are administrated parenterally. Theadministration can be an intravenous administration. The administrationcan be directly done by injection within a tumor.

To guard against possible adverse reactions, engineered immunoresponsivecells may be equipped with a transgenic safety switch, in the form of atransgene that renders the cells vulnerable to exposure to a specificsignal. For example, the herpes simplex viral thymidine kinase (TK) genemay be used in this way, for example by introduction into allogeneic Tlymphocytes used as donor lymphocyte infusions following stem celltransplantation (Greco, et al., Improving the safety of cell therapywith the TK-suicide gene. Front. Pharmacol. 2015; 6: 95). In such cells,administration of a nucleoside prodrug such as ganciclovir or acyclovircauses cell death. Alternative safety switch constructs includeinducible caspase 9, for example triggered by administration of asmall-molecule dimerizer that brings together two nonfunctional icasp9molecules to form the active enzyme. A wide variety of alternativeapproaches to implementing cellular proliferation controls have beendescribed (see U.S. Patent Publication No. 20130071414; PCT PatentPublication WO2011146862; PCT Patent Publication WO2014011987; PCTPatent Publication WO2013040371; Zhou et al. BLOOD, 2014,123/25:3895-3905; Di Stasi et al., The New England Journal of Medicine2011; 365:1673-1683; Sadelain M, The New England Journal of Medicine2011; 365:1735-173; Ramos et al., Stem Cells 28(6):1107-15 (2010)).

In a further refinement of adoptive therapies, genome editing may beused to tailor immunoresponsive cells to alternative implementations,for example providing edited CAR T cells (see Poirot et al., 2015,Multiplex genome edited T-cell manufacturing platform for“off-the-shelf” adoptive T-cell immunotherapies, Cancer Res 75 (18):3853; Ren et al., 2016, Multiplex genome editing to generate universalCAR T cells resistant to PD1 inhibition, Clin Cancer Res. 2016 Nov. 4;and Qasim et al., 2017, Molecular remission of infant B-ALL afterinfusion of universal TALEN gene-edited CAR T cells, Sci Transl Med.2017 Jan. 25; 9(374)). Cells may be edited using any CRISPR system andmethod of use thereof as described herein. CRISPR systems may bedelivered to an immune cell by any method described herein. In preferredembodiments, cells are edited ex vivo and transferred to a subject inneed thereof. Immunoresponsive cells, CAR T cells or any cells used foradoptive cell transfer may be edited. Editing may be performed forexample to insert or knock-in an exogenous gene, such as an exogenousgene encoding a CAR or a TCR, at a preselected locus in a cell; toeliminate potential alloreactive T-cell receptors (TCR) or to preventinappropriate pairing between endogenous and exogenous TCR chains, suchas to knock-out or knock-down expression of an endogenous TCR in a cell;to disrupt the target of a chemotherapeutic agent in a cell; to block animmune checkpoint, such as to knock-out or knock-down expression of animmune checkpoint protein or receptor in a cell; to knock-out orknock-down expression of other gene or genes in a cell, the reducedexpression or lack of expression of which can enhance the efficacy ofadoptive therapies using the cell; to knock-out or knock-down expressionof an endogenous gene in a cell, said endogenous gene encoding anantigen targeted by an exogenous CAR or TCR; to knock-out or knock-downexpression of one or more MHC constituent proteins in a cell; toactivate a T cell; to modulate cells such that the cells are resistantto exhaustion or dysfunction; and/or increase the differentiation and/orproliferation of functionally exhausted or dysfunctional CD8+ T-cells(see PCT Patent Publications: WO2013176915, WO2014059173, WO2014172606,WO2014184744, and WO2014191128). Editing may result in inactivation of agene.

By inactivating a gene it is intended that the gene of interest is notexpressed in a functional protein form. In a particular embodiment, theCRISPR system specifically catalyzes cleavage in one targeted genethereby inactivating said targeted gene. The nucleic acid strand breakscaused are commonly repaired through the distinct mechanisms ofhomologous recombination or non-homologous end joining (NHEJ). However,NHEJ is an imperfect repair process that often results in changes to theDNA sequence at the site of the cleavage. Repair via non-homologous endjoining (NHEJ) often results in small insertions or deletions (Indel)and can be used for the creation of specific gene knockouts. Cells inwhich a cleavage induced mutagenesis event has occurred can beidentified and/or selected by well-known methods in the art.

Hence, in certain embodiments, editing of cells (such as by CRISPR/Cas),particularly cells intended for adoptive cell therapies, moreparticularly immunoresponsive cells such as T cells, may be performed toinsert or knock-in an exogenous gene, such as an exogenous gene encodinga CAR or a TCR, at a preselected locus in a cell. Conventionally,nucleic acid molecules encoding CARs or TCRs are transfected ortransduced to cells using randomly integrating vectors, which, dependingon the site of integration, may lead to clonal expansion, oncogenictransformation, variegated transgene expression and/or transcriptionalsilencing of the transgene. Directing of transgene(s) to a specificlocus in a cell can minimize or avoid such risks and advantageouslyprovide for uniform expression of the transgene(s) by the cells. Withoutlimitation, suitable ‘safe harbor’ loci for directed transgeneintegration include CCR5 or AAVS1. Homology-directed repair (HDR)strategies are known and described elsewhere in this specificationallowing to insert transgenes into desired loci.

Further suitable loci for insertion of transgenes, in particular CAR orexogenous TCR transgenes, include without limitation loci comprisinggenes coding for constituents of endogenous T-cell receptor, such asT-cell receptor alpha locus (TRA) or T-cell receptor beta locus (TRB),for example T-cell receptor alpha constant (TRAC) locus, T-cell receptorbeta constant 1 (TRBC1) locus or T-cell receptor beta constant 2 (TRBC1)locus. Advantageously, insertion of a transgene into such locus cansimultaneously achieve expression of the transgene, potentiallycontrolled by the endogenous promoter, and knock-out expression of theendogenous TCR. This approach has been exemplified in Eyquem et al.,(2017) Nature 543: 113-117, wherein the authors used CRISPR/Cas9 geneediting to knock-in a DNA molecule encoding a CD19-specific CAR into theTRAC locus downstream of the endogenous promoter; the CAR-T cellsobtained by CRISPR were significantly superior in terms of reduced tonicCAR signaling and exhaustion.

T cell receptors (TCR) are cell surface receptors that participate inthe activation of T cells in response to the presentation of antigen.The TCR is generally made from two chains, α and β, which assemble toform a heterodimer and associates with the CD3-transducing subunits toform the T cell receptor complex present on the cell surface. Each α andβ chain of the TCR consists of an immunoglobulin-like N-terminalvariable (V) and constant (C) region, a hydrophobic transmembranedomain, and a short cytoplasmic region. As for immunoglobulin molecules,the variable region of the α and β chains are generated by V(D)Jrecombination, creating a large diversity of antigen specificitieswithin the population of T cells. However, in contrast toimmunoglobulins that recognize intact antigen, T cells are activated byprocessed peptide fragments in association with an MHC molecule,introducing an extra dimension to antigen recognition by T cells, knownas MHC restriction. Recognition of MHC disparities between the donor andrecipient through the T cell receptor leads to T cell proliferation andthe potential development of graft versus host disease (GVHD). Theinactivation of TCRα or TCRβ can result in the elimination of the TCRfrom the surface of T cells preventing recognition of alloantigen andthus GVHD. However, TCR disruption generally results in the eliminationof the CD3 signaling component and alters the means of further T cellexpansion.

Hence, in certain embodiments, editing of cells (such as by CRISPR/Cas),particularly cells intended for adoptive cell therapies, moreparticularly immunoresponsive cells such as T cells, may be performed toknock-out or knock-down expression of an endogenous TCR in a cell. Forexample, NHEJ-based or HDR-based gene editing approaches can be employedto disrupt the endogenous TCR alpha and/or beta chain genes. Forexample, gene editing system or systems, such as CRISPR/Cas system orsystems, can be designed to target a sequence found within the TCR betachain conserved between the beta 1 and beta 2 constant region genes(TRBC1 and TRBC2) and/or to target the constant region of the TCR alphachain (TRAC) gene.

Allogeneic cells are rapidly rejected by the host immune system. It hasbeen demonstrated that, allogeneic leukocytes present in non-irradiatedblood products will persist for no more than 5 to 6 days (Boni, Muranskiet al. 2008 Blood 1; 112(12):4746-54). Thus, to prevent rejection ofallogeneic cells, the host's immune system usually has to be suppressedto some extent. However, in the case of adoptive cell transfer the useof immunosuppressive drugs also have a detrimental effect on theintroduced therapeutic T cells. Therefore, to effectively use anadoptive immunotherapy approach in these conditions, the introducedcells would need to be resistant to the immunosuppressive treatment.Thus, in a particular embodiment, the present invention furthercomprises a step of modifying T cells to make them resistant to animmunosuppressive agent, preferably by inactivating at least one geneencoding a target for an immunosuppressive agent. An immunosuppressiveagent is an agent that suppresses immune function by one of severalmechanisms of action. An immunosuppressive agent can be, but is notlimited to a calcineurin inhibitor, a target of rapamycin, aninterleukin-2 receptor α-chain blocker, an inhibitor of inosinemonophosphate dehydrogenase, an inhibitor of dihydrofolic acidreductase, a corticosteroid or an immunosuppressive antimetabolite. Thepresent invention allows conferring immunosuppressive resistance to Tcells for immunotherapy by inactivating the target of theimmunosuppressive agent in T cells. As non-limiting examples, targetsfor an immunosuppressive agent can be a receptor for animmunosuppressive agent such as: CD52, glucocorticoid receptor (GR), aFKBP family gene member and a cyclophilin family gene member.

In certain embodiments, editing of cells (such as by CRISPR/Cas),particularly cells intended for adoptive cell therapies, moreparticularly immunoresponsive cells such as T cells, may be performed toblock an immune checkpoint, such as to knock-out or knock-downexpression of an immune checkpoint protein or receptor in a cell. Immunecheckpoints are inhibitory pathways that slow down or stop immunereactions and prevent excessive tissue damage from uncontrolled activityof immune cells. In certain embodiments, the immune checkpoint targetedis the programmed death-1 (PD-1 or CD279) gene (PDCD1). In otherembodiments, the immune checkpoint targeted is cytotoxicT-lymphocyte-associated antigen (CTLA-4). In additional embodiments, theimmune checkpoint targeted is another member of the CD28 and CTLA4 Igsuperfamily such as BTLA, LAG3, ICOS, PDL1 or KIR. In further additionalembodiments, the immune checkpoint targeted is a member of the TNFRsuperfamily such as CD40, OX40, CD137, GITR, CD27 or TIM-3.

Additional immune checkpoints include Src homology 2 domain-containingprotein tyrosine phosphatase 1 (SHP-1) (Watson H A, et al., SHP-1: thenext checkpoint target for cancer immunotherapy? Biochem Soc Trans. 2016Apr. 15; 44(2):356-62). SHP-1 is a widely expressed inhibitory proteintyrosine phosphatase (PTP). In T-cells, it is a negative regulator ofantigen-dependent activation and proliferation. It is a cytosolicprotein, and therefore not amenable to antibody-mediated therapies, butits role in activation and proliferation makes it an attractive targetfor genetic manipulation in adoptive transfer strategies, such aschimeric antigen receptor (CAR) T cells. Immune checkpoints may alsoinclude T cell immunoreceptor with Ig and ITIM domains(TIGIT/Vstm3/WUCAM/VSIG9) and VISTA (Le Mercier I, et al., (2015) BeyondCTLA-4 and PD-1, the generation Z of negative checkpoint regulators.Front. Immunol. 6:418).

WO2014172606 relates to the use of MT1 and/or MT2 inhibitors to increaseproliferation and/or activity of exhausted CD8+ T-cells and to decreaseCD8+ T-cell exhaustion (e.g., decrease functionally exhausted orunresponsive CD8+ immune cells). In certain embodiments,metallothioneins are targeted by gene editing in adoptively transferredT cells.

In certain embodiments, targets of gene editing may be at least onetargeted locus involved in the expression of an immune checkpointprotein. Such targets may include, but are not limited to CTLA4, PPP2CA,PPP2CB, PTPN6, PTPN22, PDCD1, ICOS (CD278), PDL1, KIR, LAG3, HAVCR2,BTLA, CD160, TIGIT, CD96, CRTAM, LAIR1, SIGLEC7, SIGLEC9, CD244 (2B4),TNFRSF10B, TNFRSF10A, CASP8, CASP10, CASP3, CASP6, CASP7, FADD, FAS,TGFBRII, TGFRBRI, SMAD2, SMAD3, SMAD4, SMAD10, SKI, SKIL, TGIF1, IL10RA,IL10RB, HMOX2, IL6R, IL6ST, EIF2AK4, CSK, PAG1, SIT1, FOXP3, PRDM1,BATF, VISTA, GUCY1A2, GUCY1A3, GUCY1B2, GUCY1B3, MT1, MT2, CD40, OX40,CD137, GITR, CD27, SHP-1, TIM-3, CEACAM-1, CEACAM-3, or CEACAM-5. Inpreferred embodiments, the gene locus involved in the expression of PD-1or CTLA-4 genes is targeted. In other preferred embodiments,combinations of genes are targeted, such as but not limited to PD-1 andTIGIT.

By means of an example and without limitation, WO2016196388 concerns anengineered T cell comprising (a) a genetically engineered antigenreceptor that specifically binds to an antigen, which receptor may be aCAR; and (b) a disrupted gene encoding a PD-L1, an agent for disruptionof a gene encoding a PD-L1, and/or disruption of a gene encoding PD-L1,wherein the disruption of the gene may be mediated by a gene editingnuclease, a zinc finger nuclease (ZFN), CRISPR/Cas9 and/or TALEN.WO2015142675 relates to immune effector cells comprising a CAR incombination with an agent (such as CRISPR, TALEN or ZFN) that increasesthe efficacy of the immune effector cells in the treatment of cancer,wherein the agent may inhibit an immune inhibitory molecule, such asPD1, PD-L1, CTLA-4, TIM-3, LAG-3, VISTA, BTLA, TIGIT, LAIR1, CD160, 2B4,TGFR beta, CEACAM-1, CEACAM-3, or CEACAM-5. Ren et al., (2017) ClinCancer Res 23 (9) 2255-2266 performed lentiviral delivery of CAR andelectro-transfer of Cas9 mRNA and gRNAs targeting endogenous TCR, β-2microglobulin (B2M) and PD1 simultaneously, to generate gene-disruptedallogeneic CAR T cells deficient of TCR, HLA class I molecule and PD1.

In certain embodiments, cells may be engineered to express a CAR,wherein expression and/or function of methylcytosine dioxygenase genes(TET1, TET2 and/or TET3) in the cells has been reduced or eliminated,such as by CRISPR, ZNF or TALEN (for example, as described inWO201704916).

In certain embodiments, editing of cells (such as by CRISPR/Cas),particularly cells intended for adoptive cell therapies, moreparticularly immunoresponsive cells such as T cells, may be performed toknock-out or knock-down expression of an endogenous gene in a cell, saidendogenous gene encoding an antigen targeted by an exogenous CAR or TCR,thereby reducing the likelihood of targeting of the engineered cells. Incertain embodiments, the targeted antigen may be one or more antigenselected from the group consisting of CD38, CD138, CS-1, CD33, CD26,CD30, CD53, CD92, CD100, CD148, CD150, CD200, CD261, CD262, CD362, humantelomerase reverse transcriptase (hTERT), survivin, mouse double minute2 homolog (MDM2), cytochrome P450 1B1 (CYP1B), HER2/neu, Wilms' tumorgene 1 (WT1), livin, alphafetoprotein (AFP), carcinoembryonic antigen(CEA), mucin 16 (MUC16), MUC1, prostate-specific membrane antigen(PSMA), p53, cyclin (D1), B cell maturation antigen (BCMA),transmembrane activator and CAML Interactor (TACI), and B-cellactivating factor receptor (BAFF-R) (for example, as described inWO2016011210 and WO2017011804).

In certain embodiments, editing of cells (such as by CRISPR/Cas),particularly cells intended for adoptive cell therapies, moreparticularly immunoresponsive cells such as T cells, may be performed toknock-out or knock-down expression of one or more MHC constituentproteins, such as one or more HLA proteins and/or beta-2 microglobulin(B2M), in a cell, whereby rejection of non-autologous (e.g., allogeneic)cells by the recipient's immune system can be reduced or avoided. Inpreferred embodiments, one or more HLA class I proteins, such as HLA-A,B and/or C, and/or B2M may be knocked-out or knocked-down. Preferably,B2M may be knocked-out or knocked-down. By means of an example, Ren etal., (2017) Clin Cancer Res 23 (9) 2255-2266 performed lentiviraldelivery of CAR and electro-transfer of Cas9 mRNA and gRNAs targetingendogenous TCR, β-2 microglobulin (B2M) and PD1 simultaneously, togenerate gene-disrupted allogeneic CAR T cells deficient of TCR, HLAclass I molecule and PD1.

In other embodiments, at least two genes are edited. Pairs of genes mayinclude, but are not limited to PD1 and TCRα, PD1 and TCRβ, CTLA-4 andTCRα, CTLA-4 and TCRβ, LAG3 and TCRα, LAG3 and TCRβ, Tim3 and TCRα, Tim3and TCRβ, BTLA and TCRα, BTLA and TCRβ, BY55 and TCRα, BY55 and TCRβ,TIGIT and TCRα, TIGIT and TCRβ, B7H5 and TCRα, B7H5 and TCRβ, LAIR1 andTCRα, LAIR1 and TCRβ, SIGLEC10 and TCRα, SIGLEC10 and TCRβ, 2B4 andTCRα, 2B4 and TCRβ.

In certain embodiments, a cell may be multiply edited (multiplex genomeediting) as taught herein to (1) knock-out or knock-down expression ofan endogenous TCR (for example, TRBC1, TRBC2 and/or TRAC), (2) knock-outor knock-down expression of an immune checkpoint protein or receptor(for example PD1, PD-L1 and/or CTLA4); and (3) knock-out or knock-downexpression of one or more MHC constituent proteins (for example, HLA-A,B and/or C, and/or B2M, preferably B2M).

Whether prior to or after genetic modification of the T cells, the Tcells can be activated and expanded generally using methods asdescribed, for example, in U.S. Pat. Nos. 6,352,694; 6,534,055;6,905,680; 5,858,358; 6,887,466; 6,905,681; 7,144,575; 7,232,566;7,175,843; 5,883,223; 6,905,874; 6,797,514; 6,867,041; and 7,572,631. Tcells can be expanded in vitro or in vivo.

Immune cells may be obtained using any method known in the art. In oneembodiment T cells that have infiltrated a tumor are isolated. T cellsmay be removed during surgery. T cells may be isolated after removal oftumor tissue by biopsy. T cells may be isolated by any means known inthe art. In one embodiment, the method may comprise obtaining a bulkpopulation of T cells from a tumor sample by any suitable method knownin the art. For example, a bulk population of T cells can be obtainedfrom a tumor sample by dissociating the tumor sample into a cellsuspension from which specific cell populations can be selected.Suitable methods of obtaining a bulk population of T cells may include,but are not limited to, any one or more of mechanically dissociating(e.g., mincing) the tumor, enzymatically dissociating (e.g., digesting)the tumor, and aspiration (e.g., as with a needle).

The bulk population of T cells obtained from a tumor sample may compriseany suitable type of T cell. Preferably, the bulk population of T cellsobtained from a tumor sample comprises tumor infiltrating lymphocytes(TILs).

The tumor sample may be obtained from any mammal. Unless statedotherwise, as used herein, the term “mammal” refers to any mammalincluding, but not limited to, mammals of the order Logomorpha, such asrabbits; the order Carnivora, including Felines (cats) and Canines(dogs); the order Artiodactyla, including Bovines (cows) and Swines(pigs); or of the order Perssodactyla, including Equines (horses). Themammals may be non-human primates, e.g., of the order Primates, Ceboids,or Simoids (monkeys) or of the order Anthropoids (humans and apes). Insome embodiments, the mammal may be a mammal of the order Rodentia, suchas mice and hamsters. Preferably, the mammal is a non-human primate or ahuman. An especially preferred mammal is the human.

T cells can be obtained from a number of sources, including peripheralblood mononuclear cells, bone marrow, lymph node tissue, spleen tissue,and tumors. In certain embodiments of the present invention, T cells canbe obtained from a unit of blood collected from a subject using anynumber of techniques known to the skilled artisan, such as Ficollseparation. In one preferred embodiment, cells from the circulatingblood of an individual are obtained by apheresis or leukapheresis. Theapheresis product typically contains lymphocytes, including T cells,monocytes, granulocytes, B cells, other nucleated white blood cells, redblood cells, and platelets. In one embodiment, the cells collected byapheresis may be washed to remove the plasma fraction and to place thecells in an appropriate buffer or media for subsequent processing steps.In one embodiment of the invention, the cells are washed with phosphatebuffered saline (PBS). In an alternative embodiment, the wash solutionlacks calcium and may lack magnesium or may lack many if not alldivalent cations. Initial activation steps in the absence of calciumlead to magnified activation. As those of ordinary skill in the artwould readily appreciate a washing step may be accomplished by methodsknown to those in the art, such as by using a semi-automated“flow-through” centrifuge (for example, the Cobe 2991 cell processor)according to the manufacturer's instructions. After washing, the cellsmay be resuspended in a variety of biocompatible buffers, such as, forexample, Ca-free, Mg-free PBS. Alternatively, the undesirable componentsof the apheresis sample may be removed and the cells directlyresuspended in culture media.

In another embodiment, T cells are isolated from peripheral bloodlymphocytes by lysing the red blood cells and depleting the monocytes,for example, by centrifugation through a PERCOLL™ gradient. A specificsubpopulation of T cells, such as CD28+, CD4+, CDC, CD45RA+, and CD45RO+T cells, can be further isolated by positive or negative selectiontechniques. For example, in one preferred embodiment, T cells areisolated by incubation with anti-CD3/anti-CD28 (i.e., 3×28)-conjugatedbeads, such as DYNABEADS® M-450 CD3/CD28 T, or XCYTE DYNABEADS™ for atime period sufficient for positive selection of the desired T cells. Inone embodiment, the time period is about 30 minutes. In a furtherembodiment, the time period ranges from 30 minutes to 36 hours or longerand all integer values there between. In a further embodiment, the timeperiod is at least 1, 2, 3, 4, 5, or 6 hours. In yet another preferredembodiment, the time period is 10 to 24 hours. In one preferredembodiment, the incubation time period is 24 hours. For isolation of Tcells from patients with leukemia, use of longer incubation times, suchas 24 hours, can increase cell yield. Longer incubation times may beused to isolate T cells in any situation where there are few T cells ascompared to other cell types, such in isolating tumor infiltratinglymphocytes (TIL) from tumor tissue or from immunocompromisedindividuals. Further, use of longer incubation times can increase theefficiency of capture of CD8+ T cells.

Enrichment of a T cell population by negative selection can beaccomplished with a combination of antibodies directed to surfacemarkers unique to the negatively selected cells. A preferred method iscell sorting and/or selection via negative magnetic immunoadherence orflow cytometry that uses a cocktail of monoclonal antibodies directed tocell surface markers present on the cells negatively selected. Forexample, to enrich for CD4+ cells by negative selection, a monoclonalantibody cocktail typically includes antibodies to CD14, CD20, CD11b,CD16, HLA-DR, and CD8.

Further, monocyte populations (i.e., CD14+ cells) may be depleted fromblood preparations by a variety of methodologies, including anti-CD14coated beads or columns, or utilization of the phagocytotic activity ofthese cells to facilitate removal. Accordingly, in one embodiment, theinvention uses paramagnetic particles of a size sufficient to beengulfed by phagocytotic monocytes. In certain embodiments, theparamagnetic particles are commercially available beads, for example,those produced by Life Technologies under the trade name Dynabeads™. Inone embodiment, other non-specific cells are removed by coating theparamagnetic particles with “irrelevant” proteins (e.g., serum proteinsor antibodies). Irrelevant proteins and antibodies include thoseproteins and antibodies or fragments thereof that do not specificallytarget the T cells to be isolated. In certain embodiments the irrelevantbeads include beads coated with sheep anti-mouse antibodies, goatanti-mouse antibodies, and human serum albumin.

In brief, such depletion of monocytes is performed by preincubating Tcells isolated from whole blood, apheresed peripheral blood, or tumorswith one or more varieties of irrelevant or non-antibody coupledparamagnetic particles at any amount that allows for removal ofmonocytes (approximately a 20:1 bead:cell ratio) for about 30 minutes to2 hours at 22 to 37 degrees C., followed by magnetic removal of cellswhich have attached to or engulfed the paramagnetic particles. Suchseparation can be performed using standard methods available in the art.For example, any magnetic separation methodology may be used including avariety of which are commercially available, (e.g., DYNAL® MagneticParticle Concentrator (DYNAL MPC®)). Assurance of requisite depletioncan be monitored by a variety of methodologies known to those ofordinary skill in the art, including flow cytometric analysis of CD14positive cells, before and after depletion.

For isolation of a desired population of cells by positive or negativeselection, the concentration of cells and surface (e.g., particles suchas beads) can be varied. In certain embodiments, it may be desirable tosignificantly decrease the volume in which beads and cells are mixedtogether (i.e., increase the concentration of cells), to ensure maximumcontact of cells and beads. For example, in one embodiment, aconcentration of 2 billion cells/ml is used. In one embodiment, aconcentration of 1 billion cells/ml is used. In a further embodiment,greater than 100 million cells/ml is used. In a further embodiment, aconcentration of cells of 10, 15, 20, 25, 30, 35, 40, 45, or 50 millioncells/ml is used. In yet another embodiment, a concentration of cellsfrom 75, 80, 85, 90, 95, or 100 million cells/ml is used. In furtherembodiments, concentrations of 125 or 150 million cells/ml can be used.Using high concentrations can result in increased cell yield, cellactivation, and cell expansion. Further, use of high cell concentrationsallows more efficient capture of cells that may weakly express targetantigens of interest, such as CD28-negative T cells, or from sampleswhere there are many tumor cells present (i.e., leukemic blood, tumortissue, etc). Such populations of cells may have therapeutic value andwould be desirable to obtain. For example, using high concentration ofcells allows more efficient selection of CD8+ T cells that normally haveweaker CD28 expression.

In a related embodiment, it may be desirable to use lower concentrationsof cells. By significantly diluting the mixture of T cells and surface(e.g., particles such as beads), interactions between the particles andcells is minimized. This selects for cells that express high amounts ofdesired antigens to be bound to the particles. For example, CD4+ T cellsexpress higher levels of CD28 and are more efficiently captured thanCD8+ T cells in dilute concentrations. In one embodiment, theconcentration of cells used is 5×106/ml. In other embodiments, theconcentration used can be from about 1×105/ml to 1×106/ml, and anyinteger value in between.

T cells can also be frozen. Wishing not to be bound by theory, thefreeze and subsequent thaw step provides a more uniform product byremoving granulocytes and to some extent monocytes in the cellpopulation. After a washing step to remove plasma and platelets, thecells may be suspended in a freezing solution. While many freezingsolutions and parameters are known in the art and will be useful in thiscontext, one method involves using PBS containing 20% DMSO and 8% humanserum albumin, or other suitable cell freezing media, the cells then arefrozen to −80° C. at a rate of 1° per minute and stored in the vaporphase of a liquid nitrogen storage tank. Other methods of controlledfreezing may be used as well as uncontrolled freezing immediately at−20° C. or in liquid nitrogen.

T cells for use in the present invention may also be antigen-specific Tcells. For example, tumor-specific T cells can be used. In certainembodiments, antigen-specific T cells can be isolated from a patient ofinterest, such as a patient afflicted with a cancer or an infectiousdisease. In one embodiment neoepitopes are determined for a subject andT cells specific to these antigens are isolated. Antigen-specific cellsfor use in expansion may also be generated in vitro using any number ofmethods known in the art, for example, as described in U.S. PatentPublication No. US 20040224402 entitled, Generation and Isolation ofAntigen-Specific T Cells, or in U.S. Pat. No. 6,040,177.Antigen-specific cells for use in the present invention may also begenerated using any number of methods known in the art, for example, asdescribed in Current Protocols in Immunology, or Current Protocols inCell Biology, both published by John Wiley & Sons, Inc., Boston, Mass.

In a related embodiment, it may be desirable to sort or otherwisepositively select (e.g. via magnetic selection) the antigen specificcells prior to or following one or two rounds of expansion. Sorting orpositively selecting antigen-specific cells can be carried out usingpeptide-MHC tetramers (Altman, et al., Science. 1996 Oct. 4;274(5284):94-6). In another embodiment the adaptable tetramer technologyapproach is used (Andersen et al., 2012 Nat Protoc. 7:891-902).Tetramers are limited by the need to utilize predicted binding peptidesbased on prior hypotheses, and the restriction to specific HLAs.Peptide-MHC tetramers can be generated using techniques known in the artand can be made with any MEW molecule of interest and any antigen ofinterest as described herein. Specific epitopes to be used in thiscontext can be identified using numerous assays known in the art. Forexample, the ability of a polypeptide to bind to MEW class I may beevaluated indirectly by monitoring the ability to promote incorporationof 125I labeled β2-microglobulin (β2m) into MEW class I/β2m/peptideheterotrimeric complexes (see Parker et al., J. Immunol. 152:163, 1994).

In one embodiment cells are directly labeled with an epitope-specificreagent for isolation by flow cytometry followed by characterization ofphenotype and TCRs. In one T cells are isolated by contacting the T cellspecific antibodies. Sorting of antigen-specific T cells, or generallyany cells of the present invention, can be carried out using any of avariety of commercially available cell sorters, including, but notlimited to, MoFlo sorter (DakoCytomation, Fort Collins, Colo.),FACSAria™, FACSArray™, FACSVantage™, BD™ LSR II, and FACSCalibur™ (BDBiosciences, San Jose, Calif.).

In a preferred embodiment, the method comprises selecting cells thatalso express CD3. The method may comprise specifically selecting thecells in any suitable manner. Preferably, the selecting is carried outusing flow cytometry. The flow cytometry may be carried out using anysuitable method known in the art. The flow cytometry may employ anysuitable antibodies and stains. Preferably, the antibody is chosen suchthat it specifically recognizes and binds to the particular biomarkerbeing selected. For example, the specific selection of CD3, CD8, TIM-3,LAG-3, 4-1BB, or PD-1 may be carried out using anti-CD3, anti-CD8,anti-TIM-3, anti-LAG-3, anti-4-1BB, or anti-PD-1 antibodies,respectively. The antibody or antibodies may be conjugated to a bead(e.g., a magnetic bead) or to a fluorochrome. Preferably, the flowcytometry is fluorescence-activated cell sorting (FACS). TCRs expressedon T cells can be selected based on reactivity to autologous tumors.Additionally, T cells that are reactive to tumors can be selected forbased on markers using the methods described in patent publication Nos.WO2014133567 and WO2014133568, herein incorporated by reference in theirentirety. Additionally, activated T cells can be selected for based onsurface expression of CD107a.

In one embodiment of the invention, the method further comprisesexpanding the numbers of T cells in the enriched cell population. Suchmethods are described in U.S. Pat. No. 8,637,307 and is hereinincorporated by reference in its entirety. The numbers of T cells may beincreased at least about 3-fold (or 4-, 5-, 6-, 7-, 8-, or 9-fold), morepreferably at least about 10-fold (or 20-, 30-, 40-, 50-, 60-, 70-, 80-,or 90-fold), more preferably at least about 100-fold, more preferably atleast about 1,000 fold, or most preferably at least about 100,000-fold.The numbers of T cells may be expanded using any suitable method knownin the art. Exemplary methods of expanding the numbers of cells aredescribed in patent publication No. WO 2003057171, U.S. Pat. No.8,034,334, and U.S. Patent Application Publication No. 2012/0244133,each of which is incorporated herein by reference.

In one embodiment, ex vivo T cell expansion can be performed byisolation of T cells and subsequent stimulation or activation followedby further expansion. In one embodiment of the invention, the T cellsmay be stimulated or activated by a single agent. In another embodiment,T cells are stimulated or activated with two agents, one that induces aprimary signal and a second that is a co-stimulatory signal. Ligandsuseful for stimulating a single signal or stimulating a primary signaland an accessory molecule that stimulates a second signal may be used insoluble form. Ligands may be attached to the surface of a cell, to anEngineered Multivalent Signaling Platform (EMSP), or immobilized on asurface. In a preferred embodiment both primary and secondary agents areco-immobilized on a surface, for example a bead or a cell. In oneembodiment, the molecule providing the primary activation signal may bea CD3 ligand, and the co-stimulatory molecule may be a CD28 ligand or4-1BB ligand.

In certain embodiments, T cells comprising a CAR or an exogenous TCR,may be manufactured as described in WO2015120096, by a methodcomprising: enriching a population of lymphocytes obtained from a donorsubject; stimulating the population of lymphocytes with one or moreT-cell stimulating agents to produce a population of activated T cells,wherein the stimulation is performed in a closed system using serum-freeculture medium; transducing the population of activated T cells with aviral vector comprising a nucleic acid molecule which encodes the CAR orTCR, using a single cycle transduction to produce a population oftransduced T cells, wherein the transduction is performed in a closedsystem using serum-free culture medium; and expanding the population oftransduced T cells for a predetermined time to produce a population ofengineered T cells, wherein the expansion is performed in a closedsystem using serum-free culture medium. In certain embodiments, T cellscomprising a CAR or an exogenous TCR, may be manufactured as describedin WO2015120096, by a method comprising: obtaining a population oflymphocytes; stimulating the population of lymphocytes with one or morestimulating agents to produce a population of activated T cells, whereinthe stimulation is performed in a closed system using serum-free culturemedium; transducing the population of activated T cells with a viralvector comprising a nucleic acid molecule which encodes the CAR or TCR,using at least one cycle transduction to produce a population oftransduced T cells, wherein the transduction is performed in a closedsystem using serum-free culture medium; and expanding the population oftransduced T cells to produce a population of engineered T cells,wherein the expansion is performed in a closed system using serum-freeculture medium. The predetermined time for expanding the population oftransduced T cells may be 3 days. The time from enriching the populationof lymphocytes to producing the engineered T cells may be 6 days. Theclosed system may be a closed bag system. Further provided is populationof T cells comprising a CAR or an exogenous TCR obtainable or obtainedby said method, and a pharmaceutical composition comprising such cells.

In certain embodiments, T cell maturation or differentiation in vitromay be delayed or inhibited by the method as described in WO2017070395,comprising contacting one or more T cells from a subject in need of a Tcell therapy with an AKT inhibitor (such as, e.g., one or a combinationof two or more AKT inhibitors disclosed in claim 8 of WO2017070395) andat least one of exogenous Interleukin-7 (IL-7) and exogenousInterleukin-15 (IL-15), wherein the resulting T cells exhibit delayedmaturation or differentiation, and/or wherein the resulting T cellsexhibit improved T cell function (such as, e.g., increased T cellproliferation; increased cytokine production; and/or increased cytolyticactivity) relative to a T cell function of a T cell cultured in theabsence of an AKT inhibitor.

In certain embodiments, a patient in need of a T cell therapy may beconditioned by a method as described in WO2016191756 comprisingadministering to the patient a dose of cyclophosphamide between 200mg/m2/day and 2000 mg/m2/day and a dose of fludarabine between 20mg/m2/day and 900 mg/m²/day.

Diseases

It will be understood by the skilled person that treating as referred toherein encompasses enhancing treatment, or improving treatment efficacy.Treatment may include inhibition of tumor regression as well asinhibition of tumor growth, metastasis or tumor cell proliferation, orinhibition or reduction of otherwise deleterious effects associated withthe tumor.

Efficaciousness of treatment is determined in association with any knownmethod for diagnosing or treating the particular disease. The inventioncomprehends a treatment method comprising any one of the methods or usesherein discussed.

The phrase “therapeutically effective amount” as used herein refers to asufficient amount of a drug, agent, or compound to provide a desiredtherapeutic effect.

As used herein “patient” refers to any human being receiving or who mayreceive medical treatment and is used interchangeably herein with theterm “subject”.

Therapy or treatment according to the invention may be performed aloneor in conjunction with another therapy, and may be provided at home, thedoctor's office, a clinic, a hospital's outpatient department, or ahospital. Treatment generally begins at a hospital so that the doctorcan observe the therapy's effects closely and make any adjustments thatare needed. The duration of the therapy depends on the age and conditionof the patient, the stage of the cancer, and how the patient responds tothe treatment.

The disclosure also provides methods for reducing resistance toimmunotherapy and treating disease. Not being bound by a theory, cancercells have many strategies of avoiding the immune system and by reducingthe signature of the present invention cancer cells may be unmasked tothe immune system. Not being bound by a theory, reducing a genesignature of the present invention may be used to treat a subject whohas not been administered an immunotherapy, such that the subject'stumor becomes unmasked to their natural or unamplified immune system. Inother embodiments, the cancer is resistant to therapies targeting theadaptive immune system (see e.g., Rooney et al., Molecular and geneticproperties of tumors associated with local immune cytolytic activity,Cell. 2015 January 15; 160(1-2): 48-61). In one embodiment, modulationof one or more of the signature genes are used for reducing animmunotherapy resistant signature for the treatment of a subpopulationof tumor cells that are linked to resistance to targeted therapies andprogressive tumor growth.

In general, the immune system is involved with controlling all cancersand the present application is applicable to treatment of all cancers.Not being bound by a theory, the signature of the present invention isapplicable to all cancers and may be used for treatment, as well as fordetermining a prognosis and stratifying patients. The cancer mayinclude, without limitation, liquid tumors such as leukemia (e.g., acuteleukemia, acute lymphocytic leukemia, acute myelocytic leukemia, acutemyeloblastic leukemia, acute promyelocytic leukemia, acutemyelomonocytic leukemia, acute monocytic leukemia, acuteerythroleukemia, chronic leukemia, chronic myelocytic leukemia, chroniclymphocytic leukemia), polycythemia vera, lymphoma (e.g., Hodgkin'sdisease, non-Hodgkin's disease), Waldenstrom's macroglobulinemia, heavychain disease, or multiple myeloma.

The cancer may include, without limitation, solid tumors such assarcomas and carcinomas. Examples of solid tumors include, but are notlimited to fibrosarcoma, myxosarcoma, liposarcoma, chondrosarcoma,osteogenic sarcoma, chordoma, angiosarcoma, endotheliosarcoma,lymphangiosarcoma, lymphangioendotheliosarcoma, synovioma, mesothelioma,Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, squamous cellcarcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma,sebaceous gland carcinoma, papillary carcinoma, papillaryadenocarcinomas, cystadenocarcinoma, medullary carcinoma, epithelialcarcinoma, bronchogenic carcinoma, hepatoma, colorectal cancer (e.g.,colon cancer, rectal cancer), anal cancer, pancreatic cancer (e.g.,pancreatic adenocarcinoma, islet cell carcinoma, neuroendocrine tumors),breast cancer (e.g., ductal carcinoma, lobular carcinoma, inflammatorybreast cancer, clear cell carcinoma, mucinous carcinoma), ovariancarcinoma (e.g., ovarian epithelial carcinoma or surfaceepithelial-stromal tumour including serous tumour, endometrioid tumorand mucinous cystadenocarcinoma, sex-cord-stromal tumor), prostatecancer, liver and bile duct carcinoma (e.g., hepatocelluar carcinoma,cholangiocarcinoma, hemangioma), choriocarcinoma, seminoma, embryonalcarcinoma, kidney cancer (e.g., renal cell carcinoma, clear cellcarcinoma, Wilm's tumor, nephroblastoma), cervical cancer, uterinecancer (e.g., endometrial adenocarcinoma, uterine papillary serouscarcinoma, uterine clear-cell carcinoma, uterine sarcomas andleiomyosarcomas, mixed mullerian tumors), testicular cancer, germ celltumor, lung cancer (e.g., lung adenocarcinoma, squamous cell carcinoma,large cell carcinoma, bronchioloalveolar carcinoma, non-small-cellcarcinoma, small cell carcinoma, mesothelioma), bladder carcinoma,signet ring cell carcinoma, cancer of the head and neck (e.g., squamouscell carcinomas), esophageal carcinoma (e.g., esophagealadenocarcinoma), tumors of the brain (e.g., glioma, glioblastoma,medullablastoma, astrocytoma, medulloblastoma, craniopharyngioma,ependymoma, pinealoma, hemangioblastoma, acoustic neuroma,oligodenroglioma, schwannoma, meningioma), neuroblastoma,retinoblastoma, neuroendocrine tumor, melanoma, cancer of the stomach(e.g., stomach adenocarcinoma, gastrointestinal stromal tumor), orcarcinoids. Lymphoproliferative disorders are also considered to beproliferative diseases.

Administration

It will be appreciated that administration of therapeutic entities inaccordance with the invention will be administered with suitablecarriers, excipients, and other agents that are incorporated intoformulations to provide improved transfer, delivery, tolerance, and thelike. A multitude of appropriate formulations can be found in theformulary known to all pharmaceutical chemists: Remington'sPharmaceutical Sciences (15th ed, Mack Publishing Company, Easton, Pa.(1975)), particularly Chapter 87 by Blaug, Seymour, therein. Theseformulations include, for example, powders, pastes, ointments, jellies,waxes, oils, lipids, lipid (cationic or anionic) containing vesicles(such as Lipofectin™), DNA conjugates, anhydrous absorption pastes,oil-in-water and water-in-oil emulsions, emulsions carbowax(polyethylene glycols of various molecular weights), semi-solid gels,and semi-solid mixtures containing carbowax. Any of the foregoingmixtures may be appropriate in treatments and therapies in accordancewith the present invention, provided that the active ingredient in theformulation is not inactivated by the formulation and the formulation isphysiologically compatible and tolerable with the route ofadministration. See also Baldrick P. “Pharmaceutical excipientdevelopment: the need for preclinical guidance.” Regul. ToxicolPharmacol. 32(2):210-8 (2000), Wang W. “Lyophilization and developmentof solid protein pharmaceuticals.” Int. J. Pharm. 203(1-2):1-60 (2000),Charman WN “Lipids, lipophilic drugs, and oral drug delivery-someemerging concepts.” J Pharm Sci. 89(8):967-78 (2000), Powell et al.“Compendium of excipients for parenteral formulations” PDA J Pharm SciTechnol. 52:238-311 (1998) and the citations therein for additionalinformation related to formulations, excipients and carriers well knownto pharmaceutical chemists.

The medicaments of the invention are prepared in a manner known to thoseskilled in the art, for example, by means of conventional dissolving,lyophilizing, mixing, granulating or confectioning processes. Methodswell known in the art for making formulations are found, for example, inRemington: The Science and Practice of Pharmacy, 20th ed., ed. A. R.Gennaro, 2000, Lippincott Williams & Wilkins, Philadelphia, andEncyclopedia of Pharmaceutical Technology, eds. J. Swarbrick and J. C.Boylan, 1988-1999, Marcel Dekker, New York.

Administration of medicaments of the invention may be by any suitablemeans that results in a compound concentration that is effective fortreating or inhibiting (e.g., by delaying) the development of a disease.The compound is admixed with a suitable carrier substance, e.g., apharmaceutically acceptable excipient that preserves the therapeuticproperties of the compound with which it is administered. One exemplarypharmaceutically acceptable excipient is physiological saline. Thesuitable carrier substance is generally present in an amount of 1-95% byweight of the total weight of the medicament. The medicament may beprovided in a dosage form that is suitable for administration. Thus, themedicament may be in form of, e.g., tablets, capsules, pills, powders,granulates, suspensions, emulsions, solutions, gels including hydrogels,pastes, ointments, creams, plasters, drenches, delivery devices,injectables, implants, sprays, or aerosols.

The agents disclosed herein (e.g., antibodies) may be used in apharmaceutical composition when combined with a pharmaceuticallyacceptable carrier. Such compositions comprise atherapeutically-effective amount of the agent and a pharmaceuticallyacceptable carrier. Such a composition may also further comprise (inaddition to an agent and a carrier) diluents, fillers, salts, buffers,stabilizers, solubilizers, and other materials well known in the art.Compositions comprising the agent can be administered in the form ofsalts provided the salts are pharmaceutically acceptable. Salts may beprepared using standard procedures known to those skilled in the art ofsynthetic organic chemistry.

The term “pharmaceutically acceptable salts” refers to salts preparedfrom pharmaceutically acceptable non-toxic bases or acids includinginorganic or organic bases and inorganic or organic acids. Salts derivedfrom inorganic bases include aluminum, ammonium, calcium, copper,ferric, ferrous, lithium, magnesium, manganic salts, manganous,potassium, sodium, zinc, and the like. Particularly preferred are theammonium, calcium, magnesium, potassium, and sodium salts. Salts derivedfrom pharmaceutically acceptable organic non-toxic bases include saltsof primary, secondary, and tertiary amines, substituted amines includingnaturally occurring substituted amines, cyclic amines, and basic ionexchange resins, such as arginine, betaine, caffeine, choline,N,N′-dibenzylethylenediamine, diethylamine, 2-diethylaminoethanol,2-dimethylaminoethanol, ethanolamine, ethylenediamine,N-ethyl-morpholine, N-ethylpiperidine, glucamine, glucosamine,histidine, hydrabamine, isopropylamine, lysine, methylglucamine,morpholine, piperazine, piperidine, polyamine resins, procaine, purines,theobromine, triethylamine, trimethylamine, tripropylamine,tromethamine, and the like. The term “pharmaceutically acceptable salt”further includes all acceptable salts such as acetate, lactobionate,benzenesulfonate, laurate, benzoate, malate, bicarbonate, maleate,bisulfate, mandelate, bitartrate, mesylate, borate, methylbromide,bromide, methylnitrate, calcium edetate, methyl sulfate, camsylate,mucate, carbonate, napsylate, chloride, nitrate, clavulanate,N-methylglucamine, citrate, ammonium salt, dihydrochloride, oleate,edetate, oxalate, edisylate, pamoate (embonate), estolate, palmitate,esylate, pantothenate, fumarate, phosphate/diphosphate, gluceptate,polygalacturonate, gluconate, salicylate, glutamate, stearate,glycollylarsanilate, sulfate, hexylresorcinate, subacetate, hydrabamine,succinate, hydrobromide, tannate, hydrochloride, tartrate,hydroxynaphthoate, teoclate, iodide, tosylate, isothionate,triethiodide, lactate, panoate, valerate, and the like which can be usedas a dosage form for modifying the solubility or hydrolysischaracteristics or can be used in sustained release or pro-drugformulations. It will be understood that, as used herein, references tospecific agents (e.g., neuromedin U receptor agonists or antagonists),also include the pharmaceutically acceptable salts thereof.

Methods of administrating the pharmacological compositions, includingagonists, antagonists, antibodies or fragments thereof, to an individualinclude, but are not limited to, intradermal, intrathecal,intramuscular, intraperitoneal, intravenous, subcutaneous, intranasal,epidural, by inhalation, and oral routes. The compositions can beadministered by any convenient route, for example by infusion or bolusinjection, by absorption through epithelial or mucocutaneous linings(for example, oral mucosa, rectal and intestinal mucosa, and the like),ocular, and the like and can be administered together with otherbiologically-active agents. Administration can be systemic or local. Inaddition, it may be advantageous to administer the composition into thecentral nervous system by any suitable route, including intraventricularand intrathecal injection. Pulmonary administration may also be employedby use of an inhaler or nebulizer, and formulation with an aerosolizingagent. It may also be desirable to administer the agent locally to thearea in need of treatment; this may be achieved by, for example, and notby way of limitation, local infusion during surgery, topicalapplication, by injection, by means of a catheter, by means of asuppository, or by means of an implant.

Various delivery systems are known and can be used to administer thepharmacological compositions including, but not limited to,encapsulation in liposomes, microparticles, microcapsules; minicells;polymers; capsules; tablets; and the like. In one embodiment, the agentmay be delivered in a vesicle, in particular a liposome. In a liposome,the agent is combined, in addition to other pharmaceutically acceptablecarriers, with amphipathic agents such as lipids which exist inaggregated form as micelles, insoluble monolayers, liquid crystals, orlamellar layers in aqueous solution. Suitable lipids for liposomalformulation include, without limitation, monoglycerides, diglycerides,sulfatides, lysolecithin, phospholipids, saponin, bile acids, and thelike. Preparation of such liposomal formulations is within the level ofskill in the art, as disclosed, for example, in U.S. Pat. Nos. 4,837,028and 4,737,323. In yet another embodiment, the pharmacologicalcompositions can be delivered in a controlled release system including,but not limited to: a delivery pump (See, for example, Saudek, et al.,New Engl. J. Med. 321: 574 (1989) and a semi-permeable polymericmaterial (See, for example, Howard, et al., J. Neurosurg. 71: 105(1989)). Additionally, the controlled release system can be placed inproximity of the therapeutic target (e.g., a tumor), thus requiring onlya fraction of the systemic dose. See, for example, Goodson, In: MedicalApplications of Controlled Release, 1984. (CRC Press, Boca Raton, Fla.).

The amount of the agents which will be effective in the treatment of aparticular disorder or condition will depend on the nature of thedisorder or condition, and may be determined by standard clinicaltechniques by those of skill within the art. In addition, in vitroassays may optionally be employed to help identify optimal dosageranges. The precise dose to be employed in the formulation will alsodepend on the route of administration, and the overall seriousness ofthe disease or disorder, and should be decided according to the judgmentof the practitioner and each patient's circumstances. Ultimately, theattending physician will decide the amount of the agent with which totreat each individual patient. In certain embodiments, the attendingphysician will administer low doses of the agent and observe thepatient's response. Larger doses of the agent may be administered untilthe optimal therapeutic effect is obtained for the patient, and at thatpoint the dosage is not increased further. In general, the daily doserange of a drug lie within the range known in the art for a particulardrug or biologic. Effective doses may be extrapolated from dose-responsecurves derived from in vitro or animal model test systems. Ultimatelythe attending physician will decide on the appropriate duration oftherapy using compositions of the present invention. Dosage will alsovary according to the age, weight and response of the individualpatient.

Methods for administering antibodies for therapeutic use is well knownto one skilled in the art. In certain embodiments, small particleaerosols of antibodies or fragments thereof may be administered (seee.g., Piazza et al., J. Infect. Dis., Vol. 166, pp. 1422-1424, 1992; andBrown, Aerosol Science and Technology, Vol. 24, pp. 45-56, 1996). Incertain embodiments, antibodies are administered in metered-dosepropellant driven aerosols. In certain embodiments, antibodies may beadministered in liposomes, i.e., immunoliposomes (see, e.g., Maruyama etal., Biochim. Biophys. Acta, Vol. 1234, pp. 74-80, 1995). In certainembodiments, immunoconjugates, immunoliposomes or immunomicrospherescontaining an agent of the present invention is administered byinhalation.

In certain embodiments, antibodies may be topically administered tomucosa, such as the oropharynx, nasal cavity, respiratory tract,gastrointestinal tract, eye such as the conjunctival mucosa, vagina,urogenital mucosa, or for dermal application. In certain embodiments,antibodies are administered to the nasal, bronchial or pulmonary mucosa.In order to obtain optimal delivery of the antibodies to the pulmonarycavity in particular, it may be advantageous to add a surfactant such asa phosphoglyceride, e.g. phosphatidylcholine, and/or a hydrophilic orhydrophobic complex of a positively or negatively charged excipient anda charged antibody of the opposite charge.

Other excipients suitable for pharmaceutical compositions intended fordelivery of antibodies to the respiratory tract mucosa may be a)carbohydrates, e.g., monosaccharides such as fructose, galactose,glucose. D-mannose, sorbiose, and the like; disaccharides, such aslactose, trehalose, cellobiose, and the like; cyclodextrins, such as2-hydroxypropyl-β-cyclodextrin; and polysaccharides, such as raffinose,maltodextrins, dextrans, and the like; b) amino acids, such as glycine,arginine, aspartic acid, glutamic acid, cysteine, lysine and the like;c) organic salts prepared from organic acids and bases, such as sodiumcitrate, sodium ascorbate, magnesium gluconate, sodium gluconate,tromethamine hydrochloride, and the like: d) peptides and proteins, suchas aspartame, human serum albumin, gelatin, and the like; e) alditols,such mannitol, xylitol, and the like, and f) polycationic polymers, suchas chitosan or a chitosan salt or derivative.

For dermal application, the antibodies of the present invention maysuitably be formulated with one or more of the following excipients:solvents, buffering agents, preservatives, humectants, chelating agents,antioxidants, stabilizers, emulsifying agents, suspending agents,gel-forming agents, ointment bases, penetration enhancers, and skinprotective agents.

Examples of solvents are e.g. water, alcohols, vegetable or marine oils(e.g. edible oils like almond oil, castor oil, cacao butter, coconutoil, corn oil, cottonseed oil, linseed oil, olive oil, palm oil, peanutoil, poppy seed oil, rapeseed oil, sesame oil, soybean oil, sunfloweroil, and tea seed oil), mineral oils, fatty oils, liquid paraffin,polyethylene glycols, propylene glycols, glycerol, liquidpolyalkylsiloxanes, and mixtures thereof.

Examples of buffering agents are e.g. citric acid, acetic acid, tartaricacid, lactic acid, hydrogenphosphoric acid, diethyl amine etc. Suitableexamples of preservatives for use in compositions are parabenes, such asmethyl, ethyl, propyl p-hydroxybenzoate, butylparaben, isobutylparaben,isopropylparaben, potassium sorbate, sorbic acid, benzoic acid, methylbenzoate, phenoxyethanol, bronopol, bronidox, MDM hydantoin,iodopropynyl butylcarbamate, EDTA, benzalconium chloride, andbenzylalcohol, or mixtures of preservatives.

Examples of humectants are glycerin, propylene glycol, sorbitol, lacticacid, urea, and mixtures thereof.

Examples of antioxidants are butylated hydroxy anisole (BHA), ascorbicacid and derivatives thereof, tocopherol and derivatives thereof,cysteine, and mixtures thereof.

Examples of emulsifying agents are naturally occurring gums, e.g. gumacacia or gum tragacanth; naturally occurring phosphatides, e.g. soybeanlecithin, sorbitan monooleate derivatives: wool fats; wool alcohols;sorbitan esters; monoglycerides; fatty alcohols; fatty acid esters (e.g.triglycerides of fatty acids); and mixtures thereof.

Examples of suspending agents are e.g. celluloses and cellulosederivatives such as, e.g., carboxymethyl cellulose,hydroxyethylcellulose, hydroxypropylcellulose,hydroxypropylmethylcellulose, carraghenan, acacia gum, arabic gum,tragacanth, and mixtures thereof.

Examples of gel bases, viscosity-increasing agents or components whichare able to take up exudate from a wound are: liquid paraffin,polyethylene, fatty oils, colloidal silica or aluminum, zinc soaps,glycerol, propylene glycol, tragacanth, carboxyvinyl polymers,magnesium-aluminum silicates, Carbopol®, hydrophilic polymers such as,e.g. starch or cellulose derivatives such as, e.g.,carboxymethylcellulose, hydroxyethylcellulose and other cellulosederivatives, water-swellable hydrocolloids, carragenans, hyaluronates(e.g. hyaluronate gel optionally containing sodium chloride), andalginates including propylene glycol alginate.

Examples of ointment bases are e.g. beeswax, paraffin, cetanol, cetylpalmitate, vegetable oils, sorbitan esters of fatty acids (Span),polyethylene glycols, and condensation products between sorbitan estersof fatty acids and ethylene oxide, e.g. polyoxyethylene sorbitanmonooleate (Tween).

Examples of hydrophobic or water-emulsifying ointment bases areparaffins, vegetable oils, animal fats, synthetic glycerides, waxes,lanolin, and liquid polyalkylsiloxanes. Examples of hydrophilic ointmentbases are solid macrogols (polyethylene glycols). Other examples ofointment bases are triethanolamine soaps, sulphated fatty alcohol andpolysorbates.

Examples of other excipients are polymers such as carmelose, sodiumcarmelose, hydroxypropylmethylcellulose, hydroxyethylcellulose,hydroxypropylcellulose, pectin, xanthan gum, locust bean gum, acaciagum, gelatin, carbomer, emulsifiers like vitamin E, glyceryl stearates,cetanyl glucoside, collagen, carrageenan, hyaluronates and alginates andchitosans.

The dose of antibody required in humans to be effective in the treatmentcancer differs with the type and severity of the cancer to be treated,the age and condition of the patient, etc. Typical doses of antibody tobe administered are in the range of 1 μg to 1 g, preferably 1-1000 morepreferably 2-500, even more preferably 5-50, most preferably 10-20 μgper unit dosage form. In certain embodiments, infusion of antibodies ofthe present invention may range from 10-500 mg/m².

There are a variety of techniques available for introducing nucleicacids into viable cells. The techniques vary depending upon whether thenucleic acid is transferred into cultured cells in vitro, or in vivo inthe cells of the intended host. Techniques suitable for the transfer ofnucleic acid into mammalian cells in vitro include the use of liposomes,electroporation, microinjection, cell fusion, DEAE-dextran, the calciumphosphate precipitation method, etc. The currently preferred in vivogene transfer techniques include transduction with viral (typicallylentivirus, adeno associated virus (AAV) and adenovirus) vectors.

In certain embodiments, an agent that reduces a gene signature asdescribed herein is used to treat a subject in need thereof having acancer.

In one embodiment, the agent is a protein kinase C (PKC) activator. By“protein kinase C activator” is meant any compound that increases thecatalytic activity of any protein kinase C (PKC) isoform (see, e.g.,WO1998017299A1). The preferred catalytic activity that is enhanced isthe kinase activity. Protein kinase C (“PKC”) is a key enzyme in signaltransduction involved in a variety of cellular functions, including cellgrowth, regulation of gene expression, and ion channel activity. The PKCfamily of isozymes includes at least 11 different protein kinases thatcan be divided into at least three subfamilies based on their homologyand sensitivity to activators. Each isozyme includes a number ofhomologous (“conserved” or “C”) domains interspersed with isozyme-unique(“variable” or “V”) domains. Members of the “classical” or “cPKC”subfamily, α, βι, βM and yPKC, contain four homologous domains (C1, C2,C3 and C4) and require calcium, phosphatidylserine, and diacylglycerolor phorbol esters for activation. In members of the “novel” or “nPKC”subfamily, δ, ε, η and θ PKC, a C2-like domain preceeds the C1 domain.However, that C2 domain does not bind calcium and therefore the nPKCsubfamily does not require calcium for activation. Finally, members ofthe “atypical” or “αPKC” subfamily, ζ and λ/iPKC, lack both the C2 andone-half of the C1 homologous domains and are insensitive todiacylglycerol, phorbol esters and calcium. Studies on the subcellulardistribution of PKC isozymes demonstrate that activation of PKC resultsin its redistribution in the cells (also termed trans location), suchthat activated PKC isozymes associate with the plasma membrane,cytoskeletal elements, nuclei, and other subcellular compartments(Saito, N. et al, Proc. Natl. Acad. Sci. USA 86:3409-3413 (1989);Papadopoulos, V. and Hall, P. F. J. Cell Biol. 108:553-567 (1989);Mochly-Rosen, D., et al., Molec. Biol. Cell (formerly Cell Reg.)1:693-706, (1990)).

Mochly-Rosen, D., et al. discusses activation of PKC (Nat Rev DrugDiscov. 2012 Dec.; 11(12): 937-957). PKC isozymes are activated by avariety of hormones, such as adrenalin and angiotensin, by growthfactors, including epidermal growth factor and insulin, and byneurotransmitters such as dopamine and endorphin; these stimulators,when bound to their respective receptors, activate members of thephospholipase C family, which generates diacylglycerol, a lipid-derivedsecond messenger. The novel isozymes (PKC δ, ε, θ and η) are activatedby diacylglycerol alone, whereas the four conventional PKC isozymes(PKCα, βII and γ) also require calcium for their activation. Cellularcalcium levels are elevated along with diacylglycerol, because thelatter is often co-produced with inositol trisphosphate (IP3), whichtriggers calcium release into the cytosol from internal stores.Activation of PKC can also occur in the absence of the above secondmessengers. High levels of cytosolic calcium can directly activatephospholipase C, thus leading to PKC activation in the absence ofreceptor activation. A number of post-translational modifications of PKCwere also found to lead to activation of select PKC isozymes both innormal and disease states. These include activation by proteolysisbetween the regulatory and the catalytic domain that was noted to occurfor PKCδ, for example. Phosphorylation of a number of sites may berequired for maturation of the newly synthesized enzyme, but also foractivation of mature isozymes, e.g. H2O2-induced tyrosinephosphorylation of PKCδ. Other modifications including oxidation,acetylation and nitration have also been found to activate PKC.

In one embodiment, the agent is an inhibitor of the NFκB pathway.Inhibitors of the NFκB pathway have been described (see, e.g., Gilmoreand Herscovitch, Inhibitors of NF-kappaB signaling: 785 and counting.Oncogene (2006) 25, 6887-6899). These compounds include chemicals,metals, metabolites, synthetic compounds, antioxidants, peptides, smallRNA/DNA, microbial and viral proteins, small molecules, and engineereddominant-negative or constitutively active polypeptides.

In one embodiment, the agent is an IGF1R inhibitor. IGF1R inhibitors arewell known in the art (see, e.g., King et al., Can we unlock thepotential of IGF-1R inhibition in cancer therapy? Cancer Treat Rev. 2014October; 40(9): 1096-1105). IGF1R inhibitors may include, but are notlimited to monoclonal anti-IGF1R antibodies, small molecule tyrosinekinase inhibitors (TKIs), and IGF ligand antibodies.

In one embodiment, the agent is Reserpine (methyl 18β-hydroxy-11,17α-dimethoxy-3β, 20α-yohimban-16β-carboxylate 3,4,5-trimethoxybenzoate)or derivative thereof. Reserpine is an alkaloid first isolated fromRauwolfia serpentina. Reserpine (also known by trade names Raudixin,Serpalan, Serpasil) is an indole alkaloid, antipsychotic, andantihypertensive drug that has been used for the control of high bloodpressure and for the relief of psychotic symptoms, although because ofthe development of better drugs for these purposes and because of itsnumerous side-effects, it is rarely used today. The antihypertensiveactions of reserpine are a result of its ability to depletecatecholamines (among other monoamine neurotransmitters) from peripheralsympathetic nerve endings. These substances are normally involved incontrolling heart rate, force of cardiac contraction and peripheralvascular resistance. The daily dose of reserpine in antihypertensivetreatment is as low as 0.1 to 0.25 mg. In certain embodiments, the doseis significantly higher for the treatment of cancer. A skilledpractitioner would know to adjust the dose based on response to thedrug. For example, reduction of an immunotherapy resistance signature ordecrease in tumor size and/or proliferation. In certain embodiments,Reserpine is administered directly to a tumor. In certain embodiments,reserpine is administered over the course of a single day or week ormonth.

Typical of the known rauwolfia alkaloids are deserpidine, alperaxylon,reserpine, and Rauwolfia serpentina. Oral dosage of the rauwolfiaalkaloid should be carefully adjusted according to individual toleranceand response, using the lowest possible effective dosage. Typically, theamount of rauwolfia alkaloid administered daily is from about 0.001 toabout 0.01 mg per kg of body weight.

In certain embodiments, the agent capable of modulating a signature asdescribed herein is a cell cycle inhibitor (see e.g., Dickson andSchwartz, Development of cell-cycle inhibitors for cancer therapy, CurrOncol. 2009 March; 16(2): 36-43). In one embodiment, the agent capableof modulating a signature as described herein is a CDK4/6 inhibitor,such as LEE011, palbociclib (PD-0332991), and Abemaciclib (LY2835219)(see, e.g., U.S. Pat. No. 9,259,399B2; WO2016025650A1; US PatentPublication No. 20140031325; US Patent Publication No. 20140080838; USPatent Publication No. 20130303543; US Patent Publication No.2007/0027147; US Patent Publication No. 2003/0229026; US PatentPublication No 2004/0048915; US Patent Publication No. 2004/0006074; USPatent Publication No. 2007/0179118; each of which is incorporated byreference herein in its entirety). Currently there are three CDK4/6inhibitors that are either approved or in late-stage development:palbociclib (PD-0332991; Pfizer), ribociclib (LEE011; Novartis), andabemaciclib (LY2835219; Lilly) (see e.g., Hamilton and Infante,Targeting CDK4/6 in patients with cancer, Cancer Treatment Reviews,Volume 45, April 2016, Pages 129-138).

In certain embodiments, an agent that reduces an immunotherapyresistance signature is co-administered with an immunotherapy or isadministered before administration of an immunotherapy. Theimmunotherapy may be adoptive cell transfer therapy, as described hereinor may be an inhibitor of any check point protein described herein.Specific check point inhibitors include, but are not limited toanti-CTLA4 antibodies (e.g., Ipilimumab), anti-PD-1 antibodies (e.g.,Nivolumab, Pembrolizumab), and anti-PD-L1 antibodies (e.g.,Atezolizumab).

In another aspect, provided is a pharmaceutical pack or kit, comprisingone or more containers filled with one or more of the ingredients of thepharmaceutical compositions.

In another aspect, provided is a kit for detecting the gene signature asdescribed herein.

With respect to general information on CRISPR-Cas Systems, componentsthereof, and delivery of such components, including methods, materials,delivery vehicles, vectors, particles, AAV, and making and usingthereof, including as to amounts and formulations, all useful in thepractice of the instant invention, reference is made to: U.S. Pat. Nos.8,999,641, 8,993,233, 8,945,839, 8,932,814, 8,906,616, 8,895,308,8,889,418, 8,889,356, 8,871,445, 8,865,406, 8,795,965, 8,771,945 and8,697,359; US Patent Publications US 2014-0310830 (U.S. application Ser.No. 14/105,031), US 2014-0287938 A1 (U.S. application Ser. No.14/213,991), US 2014-0273234 A1 (U.S. application Ser. No. 14/293,674),US2014-0273232 A1 (U.S. application Ser. No. 14/290,575), US2014-0273231 (U.S. application Ser. No. 14/259,420), US 2014-0256046 A1(U.S. application Ser. No. 14/226,274), US 2014-0248702 A1 (U.S.application Ser. No. 14/258,458), US 2014-0242700 A1 (U.S. applicationSer. No. 14/222,930), US 2014-0242699 A1 (U.S. application Ser. No.14/183,512), US 2014-0242664 A1 (U.S. application Ser. No. 14/104,990),US 2014-0234972 A1 (U.S. application Ser. No. 14/183,471), US2014-0227787 A1 (U.S. application Ser. No. 14/256,912), US 2014-0189896A1 (U.S. application Ser. No. 14/105,035), US 2014-0186958 (U.S.application Ser. No. 14/105,017), US 2014-0186919 A1 (U.S. applicationSer. No. 14/104,977), US 2014-0186843 A1 (U.S. application Ser. No.14/104,900), US 2014-0179770 A1 (U.S. application Ser. No. 14/104,837)and US 2014-0179006 A1 (U.S. application Ser. No. 14/183,486), US2014-0170753 (U.S. application Ser. No. 14/183,429); European Patents EP2 784 162 B 1 and EP 2 771 468 B1; European Patent Applications EP 2 771468 (EP13818570.7), EP 2 764 103 (EP13824232.6), and EP 2 784 162(EP14170383.5); and PCT Patent Publications PCT Patent Publications WO2014/093661 (PCT/US2013/074743), WO 2014/093694 (PCT/US2013/074790), WO2014/093595 (PCT/US2013/074611), WO 2014/093718 (PCT/US2013/074825), WO2014/093709 (PCT/US2013/074812), WO 2014/093622 (PCT/US2013/074667), WO2014/093635 (PCT/US2013/074691), WO 2014/093655 (PCT/US2013/074736), WO2014/093712 (PCT/US2013/074819), WO2014/093701 (PCT/US2013/074800),WO2014/018423 (PCT/US2013/051418), WO 2014/204723 (PCT/US2014/041790),WO 2014/204724 (PCT/US2014/041800), WO 2014/204725 (PCT/US2014/041803),WO 2014/204726 (PCT/US2014/041804), WO 2014/204727 (PCT/US2014/041806),WO 2014/204728 (PCT/US2014/041808), WO 2014/204729 (PCT/US2014/041809).Reference is also made to U.S. provisional patent applications61/758,468; 61/802,174; 61/806,375; 61/814,263; 61/819,803 and61/828,130, filed on Jan. 30, 2013; Mar. 15, 2013; Mar. 28, 2013; Apr.20, 2013; May 6, 2013 and May 28, 2013 respectively. Reference is alsomade to U.S. provisional patent application 61/836,123, filed on Jun.17, 2013. Reference is additionally made to U.S. provisional patentapplications 61/835,931, 61/835,936, 61/836,127, 61/836,101, 61/836,080and 61/835,973, each filed Jun. 17, 2013. Further reference is made toU.S. provisional patent applications 61/862,468 and 61/862,355 filed onAug. 5, 2013; 61/871,301 filed on Aug. 28, 2013; 61/960,777 filed onSep. 25, 2013 and 61/961,980 filed on Oct. 28, 2013. Reference is yetfurther made to: PCT Patent applications Nos: PCT/US2014/041803,PCT/US2014/041800, PCT/US2014/041809, PCT/US2014/041804 andPCT/US2014/041806, each filed Jun. 10, 2014 6/10/14; PCT/US2014/041808filed Jun. 11, 2014; and PCT/US2014/62558 filed Oct. 28, 2014, and U.S.Provisional Patent Applications Ser. Nos. 61/915,150, 61/915,301,61/915,267 and 61/915,260, each filed Dec. 12, 2013; 61/757,972 and61/768,959, filed on Jan. 29, 2013 and Feb. 25, 2013; 61/835,936,61/836,127, 61/836,101, 61/836,080, 61/835,973, and 61/835,931, filedJun. 17, 2013; 62/010,888 and 62/010,879, both filed Jun. 11, 2014;62/010,329 and 62/010,441, each filed Jun. 10, 2014; 61/939,228 and61/939,242, each filed Feb. 12, 2014; 61/980,012, filed Apr. 15, 2014;62/038,358, filed Aug. 17, 2014; 62/054,490, 62/055,484, 62/055,460 and62/055,487, each filed Sep. 25, 2014; and 62/069,243, filed Oct. 27,2014. Reference is also made to U.S. provisional patent applicationsNos. 62/055,484, 62/055,460, and 62/055,487, filed Sep. 25, 2014; U.S.provisional patent application 61/980,012, filed Apr. 15, 2014; and U.S.provisional patent application 61/939,242 filed Feb. 12, 2014. Referenceis made to PCT application designating, inter alia, the United States,application No. PCT/US14/41806, filed Jun. 10, 2014. Reference is madeto U.S. provisional patent application 61/930,214 filed on Jan. 22,2014. Reference is made to U.S. provisional patent applications61/915,251; 61/915,260 and 61/915,267, each filed on Dec. 12, 2013.Reference is made to US provisional patent application U.S. Ser. No.61/980,012 filed Apr. 15, 2014. Reference is made to PCT applicationdesignating, inter alia, the United States, application No.PCT/US14/41806, filed Jun. 10, 2014. Reference is made to U.S.provisional patent application 61/930,214 filed on Jan. 22, 2014.Reference is made to U.S. provisional patent applications 61/915,251;61/915,260 and 61/915,267, each filed on Dec. 12, 2013.

Mention is also made of U.S. application 62/091,455, filed, 12 Dec.2014, PROTECTED GUIDE RNAS (PGRNAS); U.S. application 62/096,708, 24Dec. 2014, PROTECTED GUIDE RNAS (PGRNAS); U.S. application 62/091,462,12 Dec. 2014, DEAD GUIDES FOR CRISPR TRANSCRIPTION FACTORS; U.S.application 62/096,324, 23 Dec. 2014, DEAD GUIDES FOR CRISPRTRANSCRIPTION FACTORS; U.S. application 62/091,456, 12 Dec. 2014,ESCORTED AND FUNCTIONALIZED GUIDES FOR CRISPR-CAS SYSTEMS; U.S.application 62/091,461, 12 Dec. 2014, DELIVERY, USE AND THERAPEUTICAPPLICATIONS OF THE CRISPR-CAS SYSTEMS AND COMPOSITIONS FOR GENOMEEDITING AS TO HEMATOPOETIC STEM CELLS (HSCs); U.S. application62/094,903, 19 Dec. 2014, UNBIASED IDENTIFICATION OF DOUBLE-STRANDBREAKS AND GENOMIC REARRANGEMENT BY GENOME-WISE INSERT CAPTURESEQUENCING; U.S. application 62/096,761, 24 Dec. 2014, ENGINEERING OFSYSTEMS, METHODS AND OPTIMIZED ENZYME AND GUIDE SCAFFOLDS FOR SEQUENCEMANIPULATION; U.S. application 62/098,059, 30 Dec. 2014, RNA-TARGETINGSYSTEM; U.S. application 62/096,656, 24 Dec. 2014, CRISPR HAVING ORASSOCIATED WITH DESTABILIZATION DOMAINS; U.S. application 62/096,697, 24Dec. 2014, CRISPR HAVING OR ASSOCIATED WITH AAV; U.S. application62/098,158, 30 Dec. 2014, ENGINEERED CRISPR COMPLEX INSERTIONALTARGETING SYSTEMS; U.S. application 62/151,052, 22 Apr. 2015, CELLULARTARGETING FOR EXTRACELLULAR EXOSOMAL REPORTING; U.S. application62/054,490, 24 Sep. 2014, DELIVERY, USE AND THERAPEUTIC APPLICATIONS OFTHE CRISPR-CAS SYSTEMS AND COMPOSITIONS FOR TARGETING DISORDERS ANDDISEASES USING PARTICLE DELIVERY COMPONENTS; U.S. application62/055,484, 25 Sep. 2014, SYSTEMS, METHODS AND COMPOSITIONS FOR SEQUENCEMANIPULATION WITH OPTIMIZED FUNCTIONAL CRISPR-CAS SYSTEMS; U.S.application 62/087,537, 4 Dec. 2014, SYSTEMS, METHODS AND COMPOSITIONSFOR SEQUENCE MANIPULATION WITH OPTIMIZED FUNCTIONAL CRISPR-CAS SYSTEMS;U.S. application 62/054,651, 24 Sep. 2014, DELIVERY, USE AND THERAPEUTICAPPLICATIONS OF THE CRISPR-CAS SYSTEMS AND COMPOSITIONS FOR MODELINGCOMPETITION OF MULTIPLE CANCER MUTATIONS IN VIVO; U.S. application62/067,886, 23 Oct. 2014, DELIVERY, USE AND THERAPEUTIC APPLICATIONS OFTHE CRISPR-CAS SYSTEMS AND COMPOSITIONS FOR MODELING COMPETITION OFMULTIPLE CANCER MUTATIONS IN VIVO; U.S. application 62/054,675, 24 Sep.2014, DELIVERY, USE AND THERAPEUTIC APPLICATIONS OF THE CRISPR-CASSYSTEMS AND COMPOSITIONS IN NEURONAL CELLS/TISSUES; U.S. application62/054,528, 24 Sep. 2014, DELIVERY, USE AND THERAPEUTIC APPLICATIONS OFTHE CRISPR-CAS SYSTEMS AND COMPOSITIONS IN IMMUNE DISEASES OR DISORDERS;U.S. application 62/055,454, 25 Sep. 2014, DELIVERY, USE AND THERAPEUTICAPPLICATIONS OF THE CRISPR-CAS SYSTEMS AND COMPOSITIONS FOR TARGETINGDISORDERS AND DISEASES USING CELL PENETRATION PEPTIDES (CPP); U.S.application 62/055,460, 25 Sep. 2014, MULTIFUNCTIONAL-CRISPR COMPLEXESAND/OR OPTIMIZED ENZYME LINKED FUNCTIONAL-CRISPR COMPLEXES; U.S.application 62/087,475, 4 Dec. 2014, FUNCTIONAL SCREENING WITH OPTIMIZEDFUNCTIONAL CRISPR-CAS SYSTEMS; U.S. application 62/055,487, 25 Sep.2014, FUNCTIONAL SCREENING WITH OPTIMIZED FUNCTIONAL CRISPR-CAS SYSTEMS;U.S. application 62/087,546, 4 Dec. 2014, MULTIFUNCTIONAL CRISPRCOMPLEXES AND/OR OPTIMIZED ENZYME LINKED FUNCTIONAL-CRISPR COMPLEXES;and U.S. application 62/098,285, 30 Dec. 2014, CRISPR MEDIATED IN VIVOMODELING AND GENETIC SCREENING OF TUMOR GROWTH AND METASTASIS.

Each of these patents, patent publications, and applications, and alldocuments cited therein or during their prosecution (“appln citeddocuments”) and all documents cited or referenced in the appln citeddocuments, together with any instructions, descriptions, productspecifications, and product sheets for any products mentioned therein orin any document therein and incorporated by reference herein, are herebyincorporated herein by reference, and may be employed in the practice ofthe invention. All documents (e.g., these patents, patent publicationsand applications and the appln cited documents) are incorporated hereinby reference to the same extent as if each individual document wasspecifically and individually indicated to be incorporated by reference.

Also with respect to general information on CRISPR-Cas Systems, mentionis made of the following (also hereby incorporated herein by reference):

-   -   Multiplex genome engineering using CRISPR/Cas systems. Cong, L.,        Ran, F. A., Cox, D., Lin, S., Barretto, R., Habib, N., Hsu, P.        D., Wu, X., Jiang, W., Marraffini, L. A., & Zhang, F. Science        Feb. 15; 339(6121):819-23 (2013);    -   RNA-guided editing of bacterial genomes using CRISPR-Cas        systems. Jiang W., Bikard D., Cox D., Zhang F, Marraffini L A.        Nat Biotechnol March; 31(3):233-9 (2013);    -   One-Step Generation of Mice Carrying Mutations in Multiple Genes        by CRISPR/Cas-Mediated Genome Engineering. Wang H., Yang H.,        Shivalila C S., Dawlaty M M., Cheng A W., Zhang F., Jaenisch R.        Cell May 9; 153(4):910-8 (2013);    -   Optical control of mammalian endogenous transcription and        epigenetic states. Konermann S, Brigham M D, Trevino A E, Hsu P        D, Heidenreich M, Cong L, Platt R J, Scott D A, Church G M,        Zhang F. Nature. August 22; 500(7463):472-6. doi:        10.1038/Nature12466. Epub 2013 Aug. 23 (2013);    -   Double Nicking by RNA-Guided CRISPR Cas9 for Enhanced Genome        Editing Specificity. Ran, F A., Hsu, P D., Lin, C Y.,        Gootenberg, J S., Konermann, S., Trevino, A E., Scott, D A.,        Inoue, A., Matoba, S., Zhang, Y., & Zhang, F. Cell August 28.        pii: S0092-8674(13)01015-5 (2013-A);    -   DNA targeting specificity of RNA-guided Cas9 nucleases. Hsu, P.,        Scott, D., Weinstein, J., Ran, F A., Konermann, S., Agarwala,        V., Li, Y., Fine, E., Wu, X., Shalem, O., Cradick, T J.,        Marraffini, L A., Bao, G., & Zhang, F. Nat Biotechnol        doi:10.1038/nbt.2647 (2013);    -   Genome engineering using the CRISPR-Cas9 system. Ran, F A., Hsu,        P D., Wright, J., Agarwala, V., Scott, D A., Zhang, F. Nature        Protocols November; 8(11):2281-308 (2013-B);    -   Genome-Scale CRISPR-Cas9 Knockout Screening in Human Cells.        Shalem, O., Sanjana, N E., Hartenian, E., Shi, X., Scott, D A.,        Mikkelson, T., Heckl, D., Ebert, B L., Root, D E., Doench, J G.,        Zhang, F. Science December 12. (2013). [Epub ahead of print];    -   Crystal structure of cas9 in complex with guide RNA and target        DNA. Nishimasu, H., Ran, F A., Hsu, P D., Konermann, S.,        Shehata, S I., Dohmae, N., Ishitani, R., Zhang, F., Nureki, O.        Cell February 27, 156(5):935-49 (2014);    -   Genome-wide binding of the CRISPR endonuclease Cas9 in mammalian        cells. Wu X., Scott D A., Kriz A J., Chiu A C., Hsu P D., Dadon        D B., Cheng A W., Trevino A E., Konermann S., Chen S., Jaenisch        R., Zhang F., Sharp P A. Nat Biotechnol. April 20. doi:        10.1038/nbt.2889 (2014);    -   CRISPR-Cas9 Knockin Mice for Genome Editing and Cancer Modeling.        Platt R J, Chen S, Zhou Y, Yim M J, Swiech L, Kempton H R,        Dahlman J E, Parnas O, Eisenhaure T M, Jovanovic M, Graham D B,        Jhunjhunwala S, Heidenreich M, Xavier R J, Langer R, Anderson D        G, Hacohen N, Regev A, Feng G, Sharp P A, Zhang F. Cell 159(2):        440-455 DOI: 10.1016/j.cell.2014.09.014 (2014);    -   Development and Applications of CRISPR-Cas9 for Genome        Engineering, Hsu P D, Lander E S, Zhang F., Cell. June 5;        157(6):1262-78 (2014).    -   Genetic screens in human cells using the CRISPR/Cas9 system,        Wang T, Wei J J, Sabatini D M, Lander E S., Science. January 3;        343(6166): 80-84. doi:10.1126/science.1246981 (2014);    -   Rational design of highly active sgRNAs for CRISPR-Cas9-mediated        gene inactivation, Doench J G, Hartenian E, Graham D B, Tothova        Z, Hegde M, Smith I, Sullender M, Ebert B L, Xavier R J, Root D        E., (published online 3 Sep. 2014) Nat Biotechnol. December;        32(12):1262-7 (2014);    -   In vivo interrogation of gene function in the mammalian brain        using CRISPR-Cas9, Swiech L, Heidenreich M, Banerjee A, Habib N,        Li Y, Trombetta J, Sur M, Zhang F., (published online 19        Oct. 2014) Nat Biotechnol. January; 33(1):102-6 (2015);    -   Genome-scale transcriptional activation by an engineered        CRISPR-Cas9 complex, Konermann S, Brigham M D, Trevino A E,        Joung J, Abudayyeh O O, Barcena C, Hsu P D, Habib N, Gootenberg        J S, Nishimasu H, Nureki O, Zhang F., Nature. January 29;        517(7536):583-8 (2015).    -   A split-Cas9 architecture for inducible genome editing and        transcription modulation, Zetsche B, Volz S E, Zhang F.,        (published online 2 Feb. 2015) Nat Biotechnol. February;        33(2):139-42 (2015);    -   Genome-wide CRISPR Screen in a Mouse Model of Tumor Growth and        Metastasis, Chen S, Sanjana N E, Zheng K, Shalem O, Lee K, Shi        X, Scott D A, Song J, Pan J Q, Weissleder R, Lee H, Zhang F,        Sharp P A. Cell 160, 1246-1260, Mar. 12, 2015 (multiplex screen        in mouse), and    -   In vivo genome editing using Staphylococcus aureus Cas9, Ran F        A, Cong L, Yan W X, Scott D A, Gootenberg J S, Kriz A J, Zetsche        B, Shalem O, Wu X, Makarova K S, Koonin E V, Sharp P A, Zhang        F., (published online 1 Apr. 2015), Nature. April 9;        520(7546):186-91 (2015).    -   Shalem et al., “High-throughput functional genomics using        CRISPR-Cas9,” Nature Reviews Genetics 16, 299-311 (May 2015).    -   Xu et al., “Sequence determinants of improved CRISPR sgRNA        design,” Genome Research 25, 1147-1157 (August 2015).    -   Parnas et al., “A Genome-wide CRISPR Screen in Primary Immune        Cells to Dissect Regulatory Networks,” Cell 162, 675-686 (Jul.        30, 2015).    -   Ramanan et al., CRISPR/Cas9 cleavage of viral DNA efficiently        suppresses hepatitis B virus,” Scientific Reports 5:10833. doi:        10.1038/srep10833 (Jun. 2, 2015)    -   Nishimasu et al., Crystal Structure of Staphylococcus aureus        Cas9,” Cell 162, 1113-1126 (Aug. 27, 2015)    -   BCL11A enhancer dissection by Cas9-mediated in situ saturating        mutagenesis, Canver et al., Nature 527(7577):192-7 (Nov.        12, 2015) doi: 10.1038/nature15521. Epub 2015 Sep. 16.    -   Cpf1 Is a Single RNA-Guided Endonuclease of a Class 2 CRISPR-Cas        System, Zetsche et al., Cell 163, 759-71 (Sep. 25, 2015).    -   Discovery and Functional Characterization of Diverse Class 2        CRISPR-Cas Systems, Shmakov et al., Molecular Cell, 60(3),        385-397 doi: 10.1016/j.molcel.2015.10.008 Epub Oct. 22, 2015.    -   Rationally engineered Cas9 nucleases with improved specificity,        Slaymaker et al., Science 2016 Jan. 1 351(6268): 84-88 doi:        10.1126/science.aad5227. Epub 2015 Dec. 1.    -   Gao et al, “Engineered Cpf1 Enzymes with Altered PAM        Specificities,” bioRxiv 091611; doi:        http://dx.doi.org/10.1101/091611 (Dec. 4, 2016).        each of which is incorporated herein by reference, may be        considered in the practice of the instant invention, and        discussed briefly below:    -   Cong et al. engineered type II CRISPR-Cas systems for use in        eukaryotic cells based on both Streptococcus thermophilus Cas9        and also Streptococcus pyogenes Cas9 and demonstrated that Cas9        nucleases can be directed by short RNAs to induce precise        cleavage of DNA in human and mouse cells. Their study further        showed that Cas9 as converted into a nicking enzyme can be used        to facilitate homology-directed repair in eukaryotic cells with        minimal mutagenic activity. Additionally, their study        demonstrated that multiple guide sequences can be encoded into a        single CRISPR array to enable simultaneous editing of several at        endogenous genomic loci sites within the mammalian genome,        demonstrating easy programmability and wide applicability of the        RNA-guided nuclease technology. This ability to use RNA to        program sequence specific DNA cleavage in cells defined a new        class of genome engineering tools. These studies further showed        that other CRISPR loci are likely to be transplantable into        mammalian cells and can also mediate mammalian genome cleavage.        Importantly, it can be envisaged that several aspects of the        CRISPR-Cas system can be further improved to increase its        efficiency and versatility.    -   Jiang et al. used the clustered, regularly interspaced, short        palindromic repeats (CRISPR)-associated Cas9 endonuclease        complexed with dual-RNAs to introduce precise mutations in the        genomes of Streptococcus pneumoniae and Escherichia coli. The        approach relied on dual-RNA:Cas9-directed cleavage at the        targeted genomic site to kill unmutated cells and circumvents        the need for selectable markers or counter-selection systems.        The study reported reprogramming dual-RNA:Cas9 specificity by        changing the sequence of short CRISPR RNA (crRNA) to make        single- and multinucleotide changes carried on editing        templates. The study showed that simultaneous use of two crRNAs        enabled multiplex mutagenesis. Furthermore, when the approach        was used in combination with recombineering, in S. pneumoniae,        nearly 100% of cells that were recovered using the described        approach contained the desired mutation, and in E. coli, 65%        that were recovered contained the mutation.    -   Wang et al. (2013) used the CRISPR/Cas system for the one-step        generation of mice carrying mutations in multiple genes which        were traditionally generated in multiple steps by sequential        recombination in embryonic stem cells and/or time-consuming        intercrossing of mice with a single mutation. The CRISPR/Cas        system will greatly accelerate the in vivo study of functionally        redundant genes and of epistatic gene interactions.    -   Konermann et al. (2013) addressed the need in the art for        versatile and robust technologies that enable optical and        chemical modulation of DNA-binding domains based CRISPR Cas9        enzyme and also Transcriptional Activator Like Effectors    -   Ran et al. (2013-A) described an approach that combined a Cas9        nickase mutant with paired guide RNAs to introduce targeted        double-strand breaks. This addresses the issue of the Cas9        nuclease from the microbial CRISPR-Cas system being targeted to        specific genomic loci by a guide sequence, which can tolerate        certain mismatches to the DNA target and thereby promote        undesired off-target mutagenesis. Because individual nicks in        the genome are repaired with high fidelity, simultaneous nicking        via appropriately offset guide RNAs is required for        double-stranded breaks and extends the number of specifically        recognized bases for target cleavage. The authors demonstrated        that using paired nicking can reduce off-target activity by 50-        to 1,500-fold in cell lines and to facilitate gene knockout in        mouse zygotes without sacrificing on-target cleavage efficiency.        This versatile strategy enables a wide variety of genome editing        applications that require high specificity.    -   Hsu et al. (2013) characterized SpCas9 targeting specificity in        human cells to inform the selection of target sites and avoid        off-target effects. The study evaluated >700 guide RNA variants        and SpCas9-induced indel mutation levels at >100 predicted        genomic off-target loci in 293T and 293FT cells. The authors        that SpCas9 tolerates mismatches between guide RNA and target        DNA at different positions in a sequence-dependent manner,        sensitive to the number, position and distribution of        mismatches. The authors further showed that SpCas9-mediated        cleavage is unaffected by DNA methylation and that the dosage of        SpCas9 and sgRNA can be titrated to minimize off-target        modification. Additionally, to facilitate mammalian genome        engineering applications, the authors reported providing a        web-based software tool to guide the selection and validation of        target sequences as well as off-target analyses.    -   Ran et al. (2013-B) described a set of tools for Cas9-mediated        genome editing via non-homologous end joining (NHEJ) or        homology-directed repair (HDR) in mammalian cells, as well as        generation of modified cell lines for downstream functional        studies. To minimize off-target cleavage, the authors further        described a double-nicking strategy using the Cas9 nickase        mutant with paired guide RNAs. The protocol provided by the        authors experimentally derived guidelines for the selection of        target sites, evaluation of cleavage efficiency and analysis of        off-target activity. The studies showed that beginning with        target design, gene modifications can be achieved within as        little as 1-2 weeks, and modified clonal cell lines can be        derived within 2-3 weeks.    -   Shalem et al. described a new way to interrogate gene function        on a genome-wide scale. Their studies showed that delivery of a        genome-scale CRISPR-Cas9 knockout (GeCKO) library targeted        18,080 genes with 64,751 unique guide sequences enabled both        negative and positive selection screening in human cells. First,        the authors showed use of the GeCKO library to identify genes        essential for cell viability in cancer and pluripotent stem        cells. Next, in a melanoma model, the authors screened for genes        whose loss is involved in resistance to vemurafenib, a        therapeutic that inhibits mutant protein kinase BRAF. Their        studies showed that the highest-ranking candidates included        previously validated genes NF1 and MED12 as well as novel hits        NF2, CUL3, TADA2B, and TADA1. The authors observed a high level        of consistency between independent guide RNAs targeting the same        gene and a high rate of hit confirmation, and thus demonstrated        the promise of genome-scale screening with Cas9.    -   Nishimasu et al. reported the crystal structure of Streptococcus        pyogenes Cas9 in complex with sgRNA and its target DNA at 2.5 A°        resolution. The structure revealed a bilobed architecture        composed of target recognition and nuclease lobes, accommodating        the sgRNA:DNA heteroduplex in a positively charged groove at        their interface. Whereas the recognition lobe is essential for        binding sgRNA and DNA, the nuclease lobe contains the HNH and        RuvC nuclease domains, which are properly positioned for        cleavage of the complementary and non-complementary strands of        the target DNA, respectively. The nuclease lobe also contains a        carboxyl-terminal domain responsible for the interaction with        the protospacer adjacent motif (PAM). This high-resolution        structure and accompanying functional analyses have revealed the        molecular mechanism of RNA-guided DNA targeting by Cas9, thus        paving the way for the rational design of new, versatile        genome-editing technologies.    -   Wu et al. mapped genome-wide binding sites of a catalytically        inactive Cas9 (dCas9) from Streptococcus pyogenes loaded with        single guide RNAs (sgRNAs) in mouse embryonic stem cells        (mESCs). The authors showed that each of the four sgRNAs tested        targets dCas9 to between tens and thousands of genomic sites,        frequently characterized by a 5-nucleotide seed region in the        sgRNA and an NGG protospacer adjacent motif (PAM). Chromatin        inaccessibility decreases dCas9 binding to other sites with        matching seed sequences; thus 70% of off-target sites are        associated with genes. The authors showed that targeted        sequencing of 295 dCas9 binding sites in mESCs transfected with        catalytically active Cas9 identified only one site mutated above        background levels. The authors proposed a two-state model for        Cas9 binding and cleavage, in which a seed match triggers        binding but extensive pairing with target DNA is required for        cleavage.    -   Platt et al. established a Cre-dependent Cas9 knockin mouse. The        authors demonstrated in vivo as well as ex vivo genome editing        using adeno-associated virus (AAV)-, lentivirus-, or        particle-mediated delivery of guide RNA in neurons, immune        cells, and endothelial cells.    -   Hsu et al. (2014) is a review article that discusses generally        CRISPR-Cas9 history from yogurt to genome editing, including        genetic screening of cells.    -   Wang et al. (2014) relates to a pooled, loss-of-function genetic        screening approach suitable for both positive and negative        selection that uses a genome-scale lentiviral single guide RNA        (sgRNA) library.    -   Doench et al. created a pool of sgRNAs, tiling across all        possible target sites of a panel of six endogenous mouse and        three endogenous human genes and quantitatively assessed their        ability to produce null alleles of their target gene by antibody        staining and flow cytometry. The authors showed that        optimization of the PAM improved activity and also provided an        on-line tool for designing sgRNAs.    -   Swiech et al. demonstrate that AAV-mediated SpCas9 genome        editing can enable reverse genetic studies of gene function in        the brain.    -   Konermann et al. (2015) discusses the ability to attach multiple        effector domains, e.g., transcriptional activator, functional        and epigenomic regulators at appropriate positions on the guide        such as stem or tetraloop with and without linkers.    -   Zetsche et al. demonstrates that the Cas9 enzyme can be split        into two and hence the assembly of Cas9 for activation can be        controlled.    -   Chen et al. relates to multiplex screening by demonstrating that        a genome-wide in vivo CRISPR-Cas9 screen in mice reveals genes        regulating lung metastasis.    -   Ran et al. (2015) relates to SaCas9 and its ability to edit        genomes and demonstrates that one cannot extrapolate from        biochemical assays.    -   Shalem et al. (2015) described ways in which catalytically        inactive Cas9 (dCas9) fusions are used to synthetically repress        (CRISPRi) or activate (CRISPRa) expression, showing. advances        using Cas9 for genome-scale screens, including arrayed and        pooled screens, knockout approaches that inactivate genomic loci        and strategies that modulate transcriptional activity.    -   Xu et al. (2015) assessed the DNA sequence features that        contribute to single guide RNA (sgRNA) efficiency in        CRISPR-based screens. The authors explored efficiency of        CRISPR/Cas9 knockout and nucleotide preference at the cleavage        site. The authors also found that the sequence preference for        CRISPRi/a is substantially different from that for CRISPR/Cas9        knockout.    -   Parnas et al. (2015) introduced genome-wide pooled CRISPR-Cas9        libraries into dendritic cells (DCs) to identify genes that        control the induction of tumor necrosis factor (Tnf) by        bacterial lipopolysaccharide (LPS). Known regulators of Tlr4        signaling and previously unknown candidates were identified and        classified into three functional modules with distinct effects        on the canonical responses to LPS.    -   Ramanan et al (2015) demonstrated cleavage of viral episomal DNA        (cccDNA) in infected cells. The HBV genome exists in the nuclei        of infected hepatocytes as a 3.2 kb double-stranded episomal DNA        species called covalently closed circular DNA (cccDNA), which is        a key component in the HBV life cycle whose replication is not        inhibited by current therapies. The authors showed that sgRNAs        specifically targeting highly conserved regions of HBV robustly        suppresses viral replication and depleted cccDNA.    -   Nishimasu et al. (2015) reported the crystal structures of        SaCas9 in complex with a single guide RNA (sgRNA) and its        double-stranded DNA targets, containing the 5′-TTGAAT-3′ PAM and        the 5′-TTGGGT-3′ PAM. A structural comparison of SaCas9 with        SpCas9 highlighted both structural conservation and divergence,        explaining their distinct PAM specificities and orthologous        sgRNA recognition.    -   Canver et al. (2015) demonstrated a CRISPR-Cas9-based functional        investigation of non-coding genomic elements. The authors        developed pooled CRISPR-Cas9 guide RNA libraries to perform in        situ saturating mutagenesis of the human and mouse BCL11A        enhancers which revealed critical features of the enhancers.    -   Zetsche et al. (2015) reported characterization of Cpf1, a class        2 CRISPR nuclease from Francisella novicida U112 having features        distinct from Cas9. Cpf1 is a single RNA-guided endonuclease        lacking tracrRNA, utilizes a T-rich protospacer-adjacent motif,        and cleaves DNA via a staggered DNA double-stranded break.    -   Shmakov et al. (2015) reported three distinct Class 2 CRISPR-Cas        systems. Two system CRISPR enzymes (C2c1 and C2c3) contain        RuvC-like endonuclease domains distantly related to Cpf1. Unlike        Cpf1, C2c1 depends on both crRNA and tracrRNA for DNA cleavage.        The third enzyme (C2c2) contains two predicted HEPN RNase        domains and is tracrRNA independent.    -   Slaymaker et al (2016) reported the use of structure-guided        protein engineering to improve the specificity of Streptococcus        pyogenes Cas9 (SpCas9). The authors developed “enhanced        specificity” SpCas9 (eSpCas9) variants which maintained robust        on-target cleavage with reduced off-target effects.

Also, “Dimeric CRISPR RNA-guided FokI nucleases for highly specificgenome editing”, Shengdar Q. Tsai, Nicolas Wyvekens, Cyd Khayter,Jennifer A. Foden, Vishal Thapar, Deepak Reyon, Mathew J. Goodwin,Martin J. Aryee, J. Keith Joung Nature Biotechnology 32(6): 569-77(2014), relates to dimeric RNA-guided FokI Nucleases that recognizeextended sequences and can edit endogenous genes with high efficienciesin human cells.

The methods and tools provided herein are may be designed for use withor Cas13, a type II nuclease that does not make use of tracrRNA.Orthologs of Cas13 have been identified in different bacterial speciesas described herein. Further type II nucleases with similar propertiescan be identified using methods described in the art (Shmakov et al.2015, 60:385-397; Abudayeh et al. 2016, Science, 5; 353(6299)). Inparticular embodiments, such methods for identifying novel CRISPReffector proteins may comprise the steps of selecting sequences from thedatabase encoding a seed which identifies the presence of a CRISPR Caslocus, identifying loci located within 10 kb of the seed comprising OpenReading Frames (ORFs) in the selected sequences, selecting therefromloci comprising ORFs of which only a single ORF encodes a novel CRISPReffector having greater than 700 amino acids and no more than 90%homology to a known CRISPR effector. In particular embodiments, the seedis a protein that is common to the CRISPR-Cas system, such as Cas1. Infurther embodiments, the CRISPR array is used as a seed to identify neweffector proteins.

One type of programmable DNA-binding domain is provided by artificialzinc-finger (ZF) technology, which involves arrays of ZF modules totarget new DNA-binding sites in the genome. Each finger module in a ZFarray targets three DNA bases. A customized array of individual zincfinger domains is assembled into a ZF protein (ZFP).

ZFPs can comprise a functional domain. The first synthetic zinc fingernucleases (ZFNs) were developed by fusing a ZF protein to the catalyticdomain of the Type IIS restriction enzyme FokI. (Kim, Y. G. et al.,1994, Chimeric restriction endonuclease, Proc. Natl. Acad. Sci. U.S.A.91, 883-887; Kim, Y. G. et al., 1996, Hybrid restriction enzymes: zincfinger fusions to Fok I cleavage domain. Proc. Natl. Acad. Sci. U.S.A.93, 1156-1160). Increased cleavage specificity can be attained withdecreased off target activity by use of paired ZFN heterodimers, eachtargeting different nucleotide sequences separated by a short spacer.(Doyon, Y. et al., 2011, Enhancing zinc-finger-nuclease activity withimproved obligate heterodimeric architectures. Nat. Methods 8, 74-79).ZFPs can also be designed as transcription activators and repressors andhave been used to target many genes in a wide variety of organisms.

In advantageous embodiments of the invention, the methods providedherein use isolated, non-naturally occurring, recombinant or engineeredDNA binding proteins that comprise TALE monomers or TALE monomers orhalf monomers as a part of their organizational structure that enablethe targeting of nucleic acid sequences with improved efficiency andexpanded specificity.

Naturally occurring TALEs or “wild type TALEs” are nucleic acid bindingproteins secreted by numerous species of proteobacteria. TALEpolypeptides contain a nucleic acid binding domain composed of tandemrepeats of highly conserved monomer polypeptides that are predominantly33, 34 or 35 amino acids in length and that differ from each othermainly in amino acid positions 12 and 13. In advantageous embodimentsthe nucleic acid is DNA. As used herein, the term “polypeptidemonomers”, “TALE monomers” or “monomers” will be used to refer to thehighly conserved repetitive polypeptide sequences within the TALEnucleic acid binding domain and the term “repeat variable di-residues”or “RVD” will be used to refer to the highly variable amino acids atpositions 12 and 13 of the polypeptide monomers. As provided throughoutthe disclosure, the amino acid residues of the RVD are depicted usingthe IUPAC single letter code for amino acids. A general representationof a TALE monomer which is comprised within the DNA binding domain isX1-11-(X12X13)-X14-33 or 34 or 35, where the subscript indicates theamino acid position and X represents any amino acid. X12X13 indicate theRVDs. In some polypeptide monomers, the variable amino acid at position13 is missing or absent and in such monomers, the RVD consists of asingle amino acid. In such cases the RVD may be alternativelyrepresented as X*, where X represents X12 and (*) indicates that X13 isabsent. The DNA binding domain comprises several repeats of TALEmonomers and this may be represented as (X1-11-(X12X13)-X14-33 or 34 or35)z, where in an advantageous embodiment, z is at least 5 to 40. In afurther advantageous embodiment, z is at least 10 to 26.

The TALE monomers have a nucleotide binding affinity that is determinedby the identity of the amino acids in its RVD. For example, polypeptidemonomers with an RVD of NI preferentially bind to adenine (A), monomerswith an RVD of NG preferentially bind to thymine (T), monomers with anRVD of HD preferentially bind to cytosine (C) and monomers with an RVDof NN preferentially bind to both adenine (A) and guanine (G). In yetanother embodiment of the invention, monomers with an RVD of IGpreferentially bind to T. Thus, the number and order of the polypeptidemonomer repeats in the nucleic acid binding domain of a TALE determinesits nucleic acid target specificity. In still further embodiments of theinvention, monomers with an RVD of NS recognize all four base pairs andmay bind to A, T, G or C. The structure and function of TALEs is furtherdescribed in, for example, Moscou et al., Science 326:1501 (2009); Bochet al., Science 326:1509-1512 (2009); and Zhang et al., NatureBiotechnology 29:149-153 (2011), each of which is incorporated byreference in its entirety.

The polypeptides used in methods of the invention are isolated,non-naturally occurring, recombinant or engineered nucleic acid-bindingproteins that have nucleic acid or DNA binding regions containingpolypeptide monomer repeats that are designed to target specific nucleicacid sequences.

As described herein, polypeptide monomers having an RVD of HN or NHpreferentially bind to guanine and thereby allow the generation of TALEpolypeptides with high binding specificity for guanine containing targetnucleic acid sequences. In a preferred embodiment of the invention,polypeptide monomers having RVDs RN, NN, NK, SN, NH, KN, HN, NQ, HH, RG,KH, RH and SS preferentially bind to guanine. In a much moreadvantageous embodiment of the invention, polypeptide monomers havingRVDs RN, NK, NQ, HH, KH, RH, SS and SN preferentially bind to guanineand thereby allow the generation of TALE polypeptides with high bindingspecificity for guanine containing target nucleic acid sequences. In aneven more advantageous embodiment of the invention, polypeptide monomershaving RVDs HH, KH, NH, NK, NQ, RH, RN and SS preferentially bind toguanine and thereby allow the generation of TALE polypeptides with highbinding specificity for guanine containing target nucleic acidsequences. In a further advantageous embodiment, the RVDs that have highbinding specificity for guanine are RN, NH RH and KH. Furthermore,polypeptide monomers having an RVD of NV preferentially bind to adenineand guanine. In more preferred embodiments of the invention, monomershaving RVDs of H*, HA, KA, N*, NA, NC, NS, RA, and S* bind to adenine,guanine, cytosine and thymine with comparable affinity.

The predetermined N-terminal to C-terminal order of the one or morepolypeptide monomers of the nucleic acid or DNA binding domaindetermines the corresponding predetermined target nucleic acid sequenceto which the polypeptides of the invention will bind. As used herein themonomers and at least one or more half monomers are “specificallyordered to target” the genomic locus or gene of interest. In plantgenomes, the natural TALE-binding sites always begin with a thymine (T),which may be specified by a cryptic signal within the non-repetitiveN-terminus of the TALE polypeptide; in some cases, this region may bereferred to as repeat 0. In animal genomes, TALE binding sites do notnecessarily have to begin with a thymine (T) and polypeptides of theinvention may target DNA sequences that begin with T, A, G or C. Thetandem repeat of TALE monomers always ends with a half-length repeat ora stretch of sequence that may share identity with only the first 20amino acids of a repetitive full length TALE monomer and this halfrepeat may be referred to as a half-monomer. Therefore, it follows thatthe length of the nucleic acid or DNA being targeted is equal to thenumber of full monomers plus two.

As described in Zhang et al., Nature Biotechnology 29:149-153 (2011),TALE polypeptide binding efficiency may be increased by including aminoacid sequences from the “capping regions” that are directly N-terminalor C-terminal of the DNA binding region of naturally occurring TALEsinto the engineered TALEs at positions N-terminal or C-terminal of theengineered TALE DNA binding region. Thus, in certain embodiments, theTALE polypeptides described herein further comprise an N-terminalcapping region and/or a C-terminal capping region.

As used herein the predetermined “N-terminus” to “C terminus”orientation of the N-terminal capping region, the DNA binding domaincomprising the repeat TALE monomers and the C-terminal capping regionprovide structural basis for the organization of different domains inthe d-TALEs or polypeptides of the invention.

The entire N-terminal and/or C-terminal capping regions are notnecessary to enhance the binding activity of the DNA binding region.Therefore, in certain embodiments, fragments of the N-terminal and/orC-terminal capping regions are included in the TALE polypeptidesdescribed herein.

In certain embodiments, the TALE polypeptides described herein contain aN-terminal capping region fragment that included at least 10, 20, 30,40, 50, 54, 60, 70, 80, 87, 90, 94, 100, 102, 110, 117, 120, 130, 140,147, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260 or 270amino acids of an N-terminal capping region. In certain embodiments, theN-terminal capping region fragment amino acids are of the C-terminus(the DNA-binding region proximal end) of an N-terminal capping region.As described in Zhang et al., Nature Biotechnology 29:149-153 (2011),N-terminal capping region fragments that include the C-terminal 240amino acids enhance binding activity equal to the full length cappingregion, while fragments that include the C-terminal 147 amino acidsretain greater than 80% of the efficacy of the full length cappingregion, and fragments that include the C-terminal 117 amino acids retaingreater than 50% of the activity of the full-length capping region.

In some embodiments, the TALE polypeptides described herein contain aC-terminal capping region fragment that included at least 6, 10, 20, 30,37, 40, 50, 60, 68, 70, 80, 90, 100, 110, 120, 127, 130, 140, 150, 155,160, 170, 180 amino acids of a C-terminal capping region. In certainembodiments, the C-terminal capping region fragment amino acids are ofthe N-terminus (the DNA-binding region proximal end) of a C-terminalcapping region. As described in Zhang et al., Nature Biotechnology29:149-153 (2011), C-terminal capping region fragments that include theC-terminal 68 amino acids enhance binding activity equal to the fulllength capping region, while fragments that include the C-terminal 20amino acids retain greater than 50% of the efficacy of the full lengthcapping region.

In certain embodiments, the capping regions of the TALE polypeptidesdescribed herein do not need to have identical sequences to the cappingregion sequences provided herein. Thus, in some embodiments, the cappingregion of the TALE polypeptides described herein have sequences that areat least 50%, 60%, 70%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%,97%, 98% or 99% identical or share identity to the capping region aminoacid sequences provided herein. Sequence identity is related to sequencehomology. Homology comparisons may be conducted by eye, or more usually,with the aid of readily available sequence comparison programs. Thesecommercially available computer programs may calculate percent (%)homology between two or more sequences and may also calculate thesequence identity shared by two or more amino acid or nucleic acidsequences. In some preferred embodiments, the capping region of the TALEpolypeptides described herein have sequences that are at least 95%identical or share identity to the capping region amino acid sequencesprovided herein.

Sequence homologies may be generated by any of a number of computerprograms known in the art, which include but are not limited to BLAST orFASTA. Suitable computer program for carrying out alignments like theGCG Wisconsin Bestfit package may also be used. Once the software hasproduced an optimal alignment, it is possible to calculate % homology,preferably % sequence identity. The software typically does this as partof the sequence comparison and generates a numerical result.

In advantageous embodiments described herein, the TALE polypeptides ofthe invention include a nucleic acid binding domain linked to the one ormore effector domains. The terms “effector domain” or “regulatory andfunctional domain” refer to a polypeptide sequence that has an activityother than binding to the nucleic acid sequence recognized by thenucleic acid binding domain. By combining a nucleic acid binding domainwith one or more effector domains, the polypeptides of the invention maybe used to target the one or more functions or activities mediated bythe effector domain to a particular target DNA sequence to which thenucleic acid binding domain specifically binds.

In some embodiments of the TALE polypeptides described herein, theactivity mediated by the effector domain is a biological activity. Forexample, in some embodiments the effector domain is a transcriptionalinhibitor (i.e., a repressor domain), such as an mSin interaction domain(SID). SID4X domain or a Kruppel-associated box (KRAB) or fragments ofthe KRAB domain. In some embodiments, the effector domain is an enhancerof transcription (i.e. an activation domain), such as the VP16, VP64 orp65 activation domain. In some embodiments, the nucleic acid binding islinked, for example, with an effector domain that includes but is notlimited to a transposase, integrase, recombinase, resolvase, invertase,protease, DNA methyltransferase, DNA demethylase, histone acetylase,histone deacetylase, nuclease, transcriptional repressor,transcriptional activator, transcription factor recruiting, proteinnuclear-localization signal or cellular uptake signal.

In some embodiments, the effector domain is a protein domain whichexhibits activities which include but are not limited to transposaseactivity, integrase activity, recombinase activity, resolvase activity,invertase activity, protease activity, DNA methyltransferase activity,DNA demethylase activity, histone acetylase activity, histonedeacetylase activity, nuclease activity, nuclear-localization signalingactivity, transcriptional repressor activity, transcriptional activatoractivity, transcription factor recruiting activity, or cellular uptakesignaling activity. Other preferred embodiments of the invention mayinclude any combination the activities described herein.

In certain embodiments, the invention involves targeted nucleic acidprofiling (e.g., sequencing, quantitative reverse transcriptionpolymerase chain reaction, and the like) (see e.g., Geiss G K, et al.,Direct multiplexed measurement of gene expression with color-coded probepairs. Nat Biotechnol. 2008 March; 26(3):317-25). In certainembodiments, a target nucleic acid molecule (e.g., RNA molecule), may besequenced by any method known in the art, for example, methods ofhigh-throughput sequencing, also known as next generation sequencing ordeep sequencing. A nucleic acid target molecule labeled with a barcode(for example, an origin-specific barcode) can be sequenced with thebarcode to produce a single read and/or contig containing the sequence,or portions thereof, of both the target molecule and the barcode.Exemplary next generation sequencing technologies include, for example,Illumina sequencing, Ion Torrent sequencing, 454 sequencing, SOLiDsequencing, and nanopore sequencing amongst others. Methods forconstructing sequencing libraries are known in the art (see, e.g., Headet al., Library construction for next-generation sequencing: Overviewsand challenges. Biotechniques. 2014; 56(2): 61-77).

In certain embodiments, the invention involves plate based single cellRNA sequencing (see, e.g., Picelli, S. et al., 2014, “Full-lengthRNA-seq from single cells using Smart-seq2” Nature protocols 9, 171-181,doi:10.1038/nprot.2014.006).

In certain embodiments, the invention involves high-throughputsingle-cell RNA-seq and/or targeted nucleic acid profiling where theRNAs from different cells are tagged individually, allowing a singlelibrary to be created while retaining the cell identity of each read. Inthis regard reference is made to Macosko et al., 2015, “Highly ParallelGenome-wide Expression Profiling of Individual Cells Using NanoliterDroplets” Cell 161, 1202-1214; International patent application numberPCT/US2015/049178, published as WO2016/040476 on Mar. 17, 2016; Klein etal., 2015, “Droplet Barcoding for Single-Cell Transcriptomics Applied toEmbryonic Stem Cells” Cell 161, 1187-1201; International patentapplication number PCT/US2016/027734, published as WO2016168584A1 onOct. 20, 2016; Zheng, et al., 2016, “Haplotyping germline and cancergenomes with high-throughput linked-read sequencing” NatureBiotechnology 34, 303-311; Zheng, et al., 2017, “Massively paralleldigital transcriptional profiling of single cells” Nat. Commun. 8, 14049doi: 10.1038/ncomms14049; International patent publication number WO2014210353 A2; Zilionis, et al., 2017, “Single-cell barcoding andsequencing using droplet microfluidics” Nat Protoc. January;12(1):44-73; Cao et al., 2017, “Comprehensive single celltranscriptional profiling of a multicellular organism by combinatorialindexing” bioRxiv preprint first posted online Feb. 2, 2017, doi:dx.doi.org/10.1101/104844; Rosenberg et al., 2017, “Scaling single celltranscriptomics through split pool barcoding” bioRxiv preprint firstposted online Feb. 2, 2017, doi: dx.doi.org/10.1101/105163; Vitak, etal., “Sequencing thousands of single-cell genomes with combinatorialindexing” Nature Methods, 14(3):302-308, 2017; Cao, et al.,Comprehensive single-cell transcriptional profiling of a multicellularorganism. Science, 357(6352):661-667, 2017; and Gierahn et al.,“Seq-Well: portable, low-cost RNA sequencing of single cells at highthroughput” Nature Methods 14, 395-398 (2017), all the contents anddisclosure of each of which are herein incorporated by reference intheir entirety.

In certain embodiments, the invention involves single nucleus RNAsequencing (sn-RNA-seq). In this regard reference is made to Swiech etal., 2014, “In vivo interrogation of gene function in the mammalianbrain using CRISPR-Cas9” Nature Biotechnology Vol. 33, pp. 102-106;Habib et al., 2016, “Div-Seq: Single-nucleus RNA-Seq reveals dynamics ofrare adult newborn neurons” Science, Vol. 353, Issue 6302, pp. 925-928;and Habib et al., 2017, “Massively parallel single-nucleus RNA-seq withDroNc-seq” Nat Methods. 2017 October; 14(10):955-958, which are hereinincorporated by reference in their entirety.

In certain embodiments, the immunotherapy resistance signature comprisesEGR1 and/or MAZ. In other embodiments, EGR1 and/or MAZ are targeted fortherapeutic intervention. In one embodiment, EGR1 and/or MAZ aretargeted to reduce a resistance signature. EGR1 and MAZ are zinc fingertranscription factors (TF). EGR1 is down regulated in malignant cells ofthe post-treatment tumors, and MAZ (Myc-associated *zinc* fingerprotein) is up-regulated. These TFs may be connected to the decrease inmetallothioneins post-treatment and availability to metal ions.Applicants saw an enrichment in EGR1 targets in the genes which aredown-regulated post-treatment. Applicants also saw an overlap with asignature identified in synovial sarcoma. In synovial sarcoma EGR1 isrepressed. Mutations in the BAF complex are strongly associated with theresponse to immunotherapy/resistance to T-cells, and is related to thepresent invention.

In certain embodiments, the gene signatures described herein arescreened by perturbation of target genes within said signatures. Incertain embodiments, perturbation of any signature gene or genedescribed herein may reduce or induce the immunotherapy resistancesignature. In preferred embodiments, the perturbed genes include MAZ,NFKBIZ, MYC, ANXA1, SOX4, MT2A, PTP4A3, CD59, DLL3, SERPINE2, SERPINF1,PERP, EGR1, SERPINA3, IFNGR2, B2M, and PDL1. In certain embodiments,after perturbation, gene expression may be evaluated to determinewhether the gene signature is reduced.

Methods and tools for genome-scale screening of perturbations in singlecells using CRISPR-Cas9 have been described, herein referred to asperturb-seq (see e.g., Dixit et al., “Perturb-Seq: Dissecting MolecularCircuits with Scalable Single-Cell RNA Profiling of Pooled GeneticScreens” 2016, Cell 167, 1853-1866; Adamson et al., “A MultiplexedSingle-Cell CRISPR Screening Platform Enables Systematic Dissection ofthe Unfolded Protein Response” 2016, Cell 167, 1867-1882; andInternational publication serial number WO/2017/075294). The presentinvention is compatible with perturb-seq, such that signature genes maybe perturbed and the perturbation may be identified and assigned to theproteomic and gene expression readouts of single cells. In certainembodiments, signature genes may be perturbed in single cells and geneexpression analyzed. Not being bound by a theory, networks of genes thatare disrupted due to perturbation of a signature gene may be determined.Understanding the network of genes effected by a perturbation may allowfor a gene to be linked to a specific pathway that may be targeted tomodulate the signature and treat a cancer. Thus, in certain embodiments,perturb-seq is used to discover novel drug targets to allow treatment ofspecific cancer patients having the gene signature of the presentinvention.

The perturbation methods and tools allow reconstructing of a cellularnetwork or circuit. In one embodiment, the method comprises (1)introducing single-order or combinatorial perturbations to a populationof cells, (2) measuring genomic, genetic, proteomic, epigenetic and/orphenotypic differences in single cells and (3) assigning aperturbation(s) to the single cells. Not being bound by a theory, aperturbation may be linked to a phenotypic change, preferably changes ingene or protein expression. In preferred embodiments, measureddifferences that are relevant to the perturbations are determined byapplying a model accounting for co-variates to the measured differences.The model may include the capture rate of measured signals, whether theperturbation actually perturbed the cell (phenotypic impact), thepresence of subpopulations of either different cells or cell states,and/or analysis of matched cells without any perturbation. In certainembodiments, the measuring of phenotypic differences and assigning aperturbation to a single cell is determined by performing single cellRNA sequencing (RNA-seq). In preferred embodiments, the single cellRNA-seq is performed by any method as described herein (e.g., Drop-seq,InDrop, 10× genomics). In certain embodiments, unique barcodes are usedto perform Perturb-seq. In certain embodiments, a guide RNA is detectedby RNA-seq using a transcript expressed from a vector encoding the guideRNA. The transcript may include a unique barcode specific to the guideRNA. Not being bound by a theory, a guide RNA and guide RNA barcode isexpressed from the same vector and the barcode may be detected byRNA-seq. Not being bound by a theory, detection of a guide RNA barcodeis more reliable than detecting a guide RNA sequence, reduces the chanceof false guide RNA assignment and reduces the sequencing cost associatedwith executing these screens. Thus, a perturbation may be assigned to asingle cell by detection of a guide RNA barcode in the cell. In certainembodiments, a cell barcode is added to the RNA in single cells, suchthat the RNA may be assigned to a single cell. Generating cell barcodesis described herein for single cell sequencing methods. In certainembodiments, a Unique Molecular Identifier (UMI) is added to eachindividual transcript and protein capture oligonucleotide. Not beingbound by a theory, the UMI allows for determining the capture rate ofmeasured signals, or preferably the binding events or the number oftranscripts captured. Not being bound by a theory, the data is moresignificant if the signal observed is derived from more than one proteinbinding event or transcript. In preferred embodiments, Perturb-seq isperformed using a guide RNA barcode expressed as a polyadenylatedtranscript, a cell barcode, and a UMI.

Perturb-seq combines emerging technologies in the field of genomeengineering, single-cell analysis and immunology, in particular theCRISPR-Cas9 system and droplet single-cell sequencing analysis. Incertain embodiments, a CRISPR system is used to create an INDEL at atarget gene. In other embodiments, epigenetic screening is performed byapplying CRISPRa/i/x technology (see, e.g., Konermann et al.“Genome-scale transcriptional activation by an engineered CRISPR-Cas9complex” Nature. 2014 Dec. 10. doi: 10.1038/nature14136; Qi, L. S., etal. (2013). “Repurposing CRISPR as an RNA-guided platform forsequence-specific control of gene expression”. Cell. 152 (5): 1173-83;Gilbert, L. A., et al., (2013). “CRISPR-mediated modular RNA-guidedregulation of transcription in eukaryotes”. Cell. 154 (2): 442-51; Komoret al., 2016, Programmable editing of a target base in genomic DNAwithout double-stranded DNA cleavage, Nature 533, 420-424; Nishida etal., 2016, Targeted nucleotide editing using hybrid prokaryotic andvertebrate adaptive immune systems, Science 353(6305); Yang et al.,2016, Engineering and optimising deaminase fusions for genome editing,Nat Commun. 7:13330; Hess et al., 2016, Directed evolution usingdCas9-targeted somatic hypermutation in mammalian cells, Nature Methods13, 1036-1042; and Ma et al., 2016, Targeted AID-mediated mutagenesis(TAM) enables efficient genomic diversification in Mammalian cellsNature Methods 13, 1029-1035). Numerous genetic variants associated withdisease phenotypes are found to be in non-coding region of the genome,and frequently coincide with transcription factor (TF) binding sites andnon-coding RNA genes. Not being bound by a theory, CRISPRa/i/xapproaches may be used to achieve a more thorough and preciseunderstanding of the implication of epigenetic regulation. In oneembodiment, a CRISPR system may be used to activate gene transcription.A nuclease-dead RNA-guided DNA binding domain, dCas9, tethered totranscriptional repressor domains that promote epigenetic silencing(e.g., KRAB) may be used for “CRISPRi” that represses transcription. Touse dCas9 as an activator (CRISPRa), a guide RNA is engineered to carryRNA binding motifs (e.g., MS2) that recruit effector domains fused toRNA-motif binding proteins, increasing transcription. A key dendriticcell molecule, p65, may be used as a signal amplifier, but is notrequired.

In certain embodiments, other CRISPR-based perturbations are readilycompatible with Perturb-seq, including alternative editors such asCRISPR/Cpf1. In certain embodiments, Perturb-seq uses Cpf1 as the CRISPRenzyme for introducing perturbations. Not being bound by a theory, Cpf1does not require Tracr RNA and is a smaller enzyme, thus allowing highercombinatorial perturbations to be tested.

The cell(s) may comprise a cell in a model non-human organism, a modelnon-human mammal that expresses a Cas protein, a mouse that expresses aCas protein, a mouse that expresses Cpf1, a cell in vivo or a cell exvivo or a cell in vitro (see e.g., WO 2014/093622 (PCT/US13/074667); USPatent Publication Nos. 20120017290 and 20110265198 assigned to SangamoBioSciences, Inc.; US Patent Publication No. 20130236946 assigned toCellectis; Platt et al., “CRISPR-Cas9 Knockin Mice for Genome Editingand Cancer Modeling” Cell (2014), 159(2): 440-455; “Oncogenic modelsbased on delivery and use of the CRISPR-Cas systems, vectors andcompositions” WO2014204723A1 “Delivery and use of the CRISPR-Cassystems, vectors and compositions for hepatic targeting and therapy”WO2014204726A1; “Delivery, use and therapeutic applications of theCRISPR-Cas systems and compositions for modeling mutations inleukocytes” WO2016049251; and Chen et al., “Genome-wide CRISPR Screen ina Mouse Model of Tumor Growth and Metastasis” 2015, Cell 160,1246-1260). The cell(s) may also comprise a human cell. Mouse cell linesmay include, but are not limited to neuro-2a cells and EL4 cell lines(ATCC TIB-39). Primary mouse T cells may be isolated from C57/BL6 mice.Primary mouse T cells may be isolated from Cas9-expressing mice.

In one embodiment, CRISPR/Cas9 may be used to perturb protein-codinggenes or non-protein-coding DNA. CRISPR/Cas9 may be used to knockoutprotein-coding genes by frameshifts, point mutations, inserts, ordeletions. An extensive toolbox may be used for efficient and specificCRISPR/Cas9 mediated knockout as described herein, including adouble-nicking CRISPR to efficiently modify both alleles of a targetgene or multiple target loci and a smaller Cas9 protein for delivery onsmaller vectors (Ran, F. A., et al., In vivo genome editing usingStaphylococcus aureus Cas9. Nature. 520, 186-191 (2015)). A genome-widesgRNA mouse library (−10 sgRNAs/gene) may also be used in a mouse thatexpresses a Cas9 protein (see, e.g., WO2014204727A1).

In one embodiment, perturbation is by deletion of regulatory elements.Non-coding elements may be targeted by using pairs of guide RNAs todelete regions of a defined size, and by tiling deletions covering setsof regions in pools.

In one embodiment, perturbation of genes is by RNAi. The RNAi may beshRNA's targeting genes. The shRNA's may be delivered by any methodsknown in the art. In one embodiment, the shRNA's may be delivered by aviral vector. The viral vector may be a lentivirus, adenovirus, or adenoassociated virus (AAV).

A CRISPR system may be delivered to primary mouse T-cells. Over 80%transduction efficiency may be achieved with Lenti-CRISPR constructs inCD4 and CD8 T-cells. Despite success with lentiviral delivery, recentwork by Hendel et al, (Nature Biotechnology 33, 985-989 (2015)doi:10.1038/nbt.3290) showed the efficiency of editing human T-cellswith chemically modified RNA, and direct RNA delivery to T-cells viaelectroporation. In certain embodiments, perturbation in mouse primaryT-cells may use these methods.

In certain embodiments, whole genome screens can be used forunderstanding the phenotypic readout of perturbing potential targetgenes. In preferred embodiments, perturbations target expressed genes asdefined by a gene signature using a focused sgRNA library. Libraries maybe focused on expressed genes in specific networks or pathways. In otherpreferred embodiments, regulatory drivers are perturbed. In certainembodiments, Applicants perform systematic perturbation of key genesthat regulate T-cell function in a high-throughput fashion. In certainembodiments, Applicants perform systematic perturbation of key genesthat regulate cancer cell function in a high-throughput fashion (e.g.,immune resistance or immunotherapy resistance). Applicants can use geneexpression profiling data to define the target of interest and performfollow-up single-cell and population RNA-seq analysis. Not being boundby a theory, this approach will accelerate the development oftherapeutics for human disorders, in particular cancer. Not being boundby a theory, this approach will enhance the understanding of the biologyof T-cells and tumor immunity, and accelerate the development oftherapeutics for human disorders, in particular cancer, as describedherein.

Not being bound by a theory, perturbation studies targeting the genesand gene signatures described herein could (1) generate new insightsregarding regulation and interaction of molecules within the system thatcontribute to suppression of an immune response, such as in the casewithin the tumor microenvironment, and (2) establish potentialtherapeutic targets or pathways that could be translated into clinicalapplication.

In certain embodiments, after determining Perturb-seq effects in cancercells and/or primary T-cells, the cells are infused back to the tumorxenograft models (melanoma, such as B16F10 and colon cancer, such asCT26) to observe the phenotypic effects of genome editing. Not beingbound by a theory, detailed characterization can be performed based on(1) the phenotypes related to tumor progression, tumor growth, immuneresponse, etc. (2) the TILs that have been genetically perturbed byCRISPR-Cas9 can be isolated from tumor samples, subject to cytokineprofiling, qPCR/RNA-seq, and single-cell analysis to understand thebiological effects of perturbing the key driver genes within thetumor-immune cell contexts. Not being bound by a theory, this will leadto validation of TILs biology as well as lead to therapeutic targets.

The invention is further described in the following examples, which donot limit the scope of the invention described in the claims.

EXAMPLES Example 1—Identifying Signatures of Resistance

Applicants leveraged single-cell RNA-sequencing (scRNA-Seq) of thousandsof cells from melanoma tumors and a novel data-driven method tosystematically map cancer programs that promote ICR and T cellexclusion. Applicants collected 10,123 scRNA-seq profiles from thetumors of 31 patients, consisting of 15 treatment naïve (TN) patients,and 16 post-ICI tumors. Of these 16 post-ICI specimens, 15 had clinicalresistance and were therefore termed ICI-resistant (ICR), and one had apartial response (PR) according to the RECIST criteria (18) (FIG. 1A,table 51), and was termed as having clinical benefit (CB). Applicantsfiltered lower-quality cells to retain 7,186 high-qualitytranscriptomes, including 4,199 cells from 16 patients that Applicantspreviously reported (13), and 2,987 cells from 16 newly collectedpatient tumors (table 51).

Applicants first aimed to determine the effects ICI has on differentcell types in the tumor at the time of post-ICI progression, bycomparing between the ICR and TN tumors. Although the specimens in thedifferent treatment groups were not from the same patients, Applicantsreasoned that the high resolution and large number of cells profiledwill provide sufficient power to detect some of these effects.

It revealed that, despite the lack of clinical response, CD8 T-cells inthe ICR tumors manifested heterogeneous phenotypes of T-cell activation.Conversely, the malignant cells of ICR tumors had a distincttranscriptional state that was substantially less frequent in the TNtumors.

Next, for any such transcriptional program that may reflect ICI effects,Applicants examined its potential causal connection to immune evasion orresistance. Applicants acknowledged the possibility that malignant cellsderived from TN tumors could contain both treatment-sensitive andintrinsically resistant cells. Thus, Applicants tested the malignantsignatures in two independent validation cohorts (FIG. 1A), wherepre-ICI patient biopsies were profiled with bulk RNA-Seq, and theresponse to ICI therapy was monitored. Applicants demonstrated that thisoncogenic state is tightly linked to immune evasion and exclusion, andthat it can be used to predict ICR based on the bulk RNA-seq of thepre-ICI biopsy.

Applicants collected scRNA-Seq of dissociated individual cells fromfresh tumor resections, sorted into immune and non-immune cells based onthe CD45 expression, and profiled them with a modified full-lengthSMART-Seq2 protocol (materials and methods, table S2). Applicantsdistinguished different cell subsets and clones both by their expressionprofiles and by their inferred genetic features. In the non-immunecompartment (FIG. 1B), Applicants distinguished malignant fromnon-malignant cells (materials and methods) according to (1) theirinferred CNV profiles (13) (FIG. 5); (2) under-expression of differentnon-malignant cell-type signatures (FIG. 5B); and (3) high similarity tobulk RNA-Seq profiles of melanoma tumors compared to adjacent normaltissue. The cell assignments by the different criteria were highlyconsistent (hypergeometric p-value <10⁻¹⁷, FIG. 5, materials andmethods). Within non-malignant cells, Applicants used unsupervisedclustering to identify (materials and methods) CD8 and CD4 T cells, Bcells, NK cells, macrophages, Cancer Associated Fibroblasts (CAFs) andendothelial cells (FIG. 1C, FIG. 6, table S4). Overall, malignant cellsclustered first by their tumor of origin (FIG. 1B), while thenon-malignant cells clustered primarily by their cell type, and onlythen by their tumor of origin (FIG. 1C).

Applicants identified transcriptional features that distinguish betweenthe cells of TN and ICR tumors, analyzing separately each cell type witha sufficient number (>100) of cells: malignant cells, macrophages, Bcells, CD8 T cells, and CD4 T cells. Applicants applied a subsamplingprocedure to prevent tumors with a particularly large number of cells ofa given type from dominating the results and to mitigate the effects ofoutliers. For each cell type Applicants defined an ICR-up and ICR-downsignature, consisting of genes that were significantly up or downregulated in the cells from the ICR tumors, respectively (19).Applicants used a mixed-effect model to test the ability of a given genesignature to distinguish between cells from ICR and TN tumors, whileaccounting for potential confounders, including other clinicalcharacteristics and cell quality (materials and methods).

The CD8 T cells and malignant cells subset derived from ICR patientswere markedly different from their TN counterparts (FIG. 7, tables S5and S6), and are the focus of this analysis. Macrophages also showed ICRassociated expression programs (table S5), but due to their relativelysmall number in the dataset, Applicants did not pursue them further.Conversely, very few genes where differentially expressed between theICR vs. TN groups when analyzing B cells or CD4 T cells (table S5).Deeper sampling of these and other cell types might identify significantdistinctions.

The CD8 T-cell-ICR signatures (FIG. 1D) revealed the induction ofcytotoxicity genes and the repression of some exhaustion features.Compared to TN CD8 T cells, ICR CD8 T cells up regulated the T cellactivation markers STAT1, GBP2, GBP5 and IRF1, and down regulated WNK1.Inhibition of WNK1 has been shown to increase T cell infiltration andaccumulation in tumors in an in vivo shRNA screen (20). Lactatedehydrogenase A (LDHA) was also up regulated in the ICR CD8 T cells,suggesting that the cells may have infiltrated the hypoxic tumormicroenvironment. Among the immune checkpoints, HAVCR2 (TIM3) and CD27are significantly, though modestly, down-regulated. Although theinhibitory checkpoints CTLA-4, TIGIT, LAG-3, PD-1, and TIM3 co-varyacross cells (along with the transcription factor PRDM1), as Applicantspreviously reported (13, 21), Applicants did not detect a significantdifference in their expression between TN and ICR cells (FIG. 8A).Rather, CD8 T cells from both TN and ICR tumor specimens spanned aspectrum of states in the exhaustion-cytotoxicity space, even within theCD8 T cells of the same tumor (13), with a strong association betweendysfunction (“exhaustion”) and cytotoxicity scores at the single celllevel (FIG. 1E, FIG. 8B), as Applicants previously reported (13).Notably, the CD8 T cells of the one ICI responder patient are bothhighly cytotoxic and significantly less dysfunctional than cells ofother patients (FIG. 1E, P=1.31*10⁻⁶, hypergeometric test). However,since a similar trend was observed in one of the ICR patients (Mel126,P=4.08*10⁻¹³, hypergeometric test), such an enhanced cytotoxic state maynot necessarily mark clinical response. These findings were robust whenusing different T cell dysfunction signatures (materials and methods),including single-cell signatures that were recently identified in Tcells from hepatocellular carcinoma tumors (22) (FIG. 8B, P<2.46*10⁻⁴,hypergeometric test). A list of differentially expressed genes obtainedwhen comparing the CD8 T cells of the CB patients to those from the ICRpatients is provided in table S7.

To examine the association between CD8 T cell profiles and clonalexpansion Applicants reconstructed full-length T cell receptors (TCR)and identified 137 CD8 T cell clones of varying sizes (23) (FIG. 1F,FIG. 9). Three patients, all of them ICR, had exceptionally large clonalexpansions, with 39-51% of the CD8 T cells in these tumors as members oflarge (>20 cells) clonotypes (FIG. 1F). These three ICR patients hadextremely expanded CD8 T cells, even after controlling for the number ofCD8 T cells profiled and the success rate of TCR reconstruction(materials and methods, P=4.54*10⁻³, one-sided Wilcoxon ranksum, FIG.9B). For one ICR patient with extreme clonal expansions, Applicantsobtained two lesions a year apart: 15 of the 28 clones identified inthese specimens included cells from both lesions, such that 71% and 52%of the CD8 T cells in the early and late samples, respectively, were inthe shared clones, demonstrating their stability and persistence (FIG.9C,D). T cell clonality pre-treatment has previously been identified asa potential predictive marker of response to anti-PD-1 therapy (6); theresults herein suggest that the extent of clonal expansion post ICI maynot be coupled to clinical response.

The expression of the ICR signature is higher in expanded CD8 T cellswithin each subset of patients, with the clonally expanded ICR CD8 Tcells scoring highest (FIG. 1D,G, left, P=3.23*10⁻⁵, mixed-effectstest). Nonetheless, even when completely removing the T cells of thethree ICR patients with the large T cell clonal expansion, theT-cell-ICR signature still significantly distinguished between the TNand ICR CD8 T cells (FIG. 1G, right, P=5.56*10⁻⁵³ and 7.41*10⁻³, t-testand mixed effects test, respectively). The expanded T cells had a genesignature that included significant down-regulation of KLRG1 (tableS11).

According to the expression of cell cycle signatures in each cell(materials and methods), five patients had a significantly largerfraction of cycling CD8 T cells (hypergeometric p-value <0.01), four ofthem were ICR patients. Proliferating CD8 T cells expressed some uniquegenes compared to proliferating malignant cells (FIG. 1H, table S8),including induction of oxidative phosphorylation (P=7.89*10⁻⁶,hypergeometric test) and repression of the hematopoietic lineage genesCD37, IL11RA, and IL7R (P=1.28*10⁻⁴, hypergeometric test). Thus, it maybe possible to perturb T cell proliferation specifically, withoutaffecting tumor cells (i.e. tumor growth).

Taken together, these findings demonstrate that even in ICR patients CD8T cells following ICI can show some indicators of enhancedfunctionality, such as expansion and transcriptional changes. In otherwords, these findings demonstrate that ICI can promote the expansion andfunctionality of the CD8 T cells without leading to a clinical response.Additional data from ICI responders is needed to examine if insufficientT cell functionality nonetheless limited the clinical response in suchICR patients. Nevertheless, Applicants hypothesized that the malignantcell compartment may contribute to ICR in these patients, at least inpart.

Applicants thus turned to examine the effect of ICI on the malignantcell profiles, and identified signatures that distinguish malignantcells from ICR vs. TN tumors: oncogenic-ICR-up and oncogenic-ICR-down(FIG. 2A,B, table S6). The signatures were robust and generalizable incross-validation (withholding data from each patient in turn andclassifying the withheld test set; materials and methods, FIG. 2A,AUC=0.86). The variation in the expression of the oncogenic-ICRsignatures in either this data or across TCGA melanoma bulk tumors wasnot significantly associated with potential confounders (materials andmethods, mixed-effect model and ANOVA, respectively). Finally, aproportion of malignant cells in TN tumors manifested the oncogenic-ICRstate (FIG. 2B, right), suggesting that it may precede ICI at least insome patients. This is discussed further below.

The oncogenic-ICR-down signature genes were enriched both in pathwaysthat reflect established mechanisms of resistance, includingdownregulation of IFN-signaling and MEW class I presentation (8), and inadditional processes, not previously implicated in ICR (FIG. 2B, tablesS6 and S9, materials and methods). These include suppression of otherinnate immune responses, such as TNF-αmediated NF B signaling,apoptosis, response to the complement system, IL2/STAT5 signaling, andthe reduced expression of metallothioneins. NFκB pathway activation caninduce expression of cytokines with either negative or positiveimmune-modulatory effects (24, 25). Our results suggest thatunder-expression of TNF-αmediated NFκB signaling genes may bedetrimental for response. The oncogenic-ICR-up genes include severaltranscriptional and chromatin regulators (e.g., SNAI2, HMGA1), and areenriched for Myc and CDK7/8 targets (P<10⁻¹¹, hypergeometric p-value).Myc-activation has been previously linked to increased expression ofimmunosuppressive signals, including the upregulation of PD-L1 andβ-catenin, which in turn inhibits dendritic cell recruitment to thetumor microenvironment via CCL4 (11). Similar results were obtained whencomparing pre-defined gene modules directly between malignant cells ofICR and TN patients (FIG. 2C, materials and methods), includingrepression of the IL6/JAK/STAT3 pathway; mutations in this pathway wererecently reported as an escape mechanism to anti-PD-1 therapy (8).

Gene modules are more robust to noise and provide more coherent signalsthan the expression of single genes. Applicants thus applied themixed-effect models to test which biological pathways are differentiallyexpressed between the two groups. The analysis revealed similar pathwaysto those outlined above, as well as the repression of the JAK/STATpathway. Mutations in this pathway were previously reported as an escapemechanism to anti-PD-1 therapy.

Several lines of evidence suggest that the oncogenic-ICR-up andoncogenic-ICR-down signatures are under shared control by one or fewmaster regulators with opposing effects on these two programs. First,the expression of the oncogenic-ICR-up and oncogenic-ICR-down signaturesis anti-correlated within the malignant cells of the same tumor andacross hundreds of (TCGA) melanoma tumors (FIG. 2D,E). Second, in theConnectivity Map (26), there is a significant overlap between thegenetic perturbations that induce the oncogenic-ICR-down signature andthose that repress the oncogenic-ICR-up signature (hypergeometricp-value=1.9*10⁻⁶), including overexpression of IFN-γ and IFN-β and theknockdown of MYC. Indeed, MYC knockdown is the top perturbation torepress oncogenic-ICR-up, which is enriched for Myc targets. Moreover,there are 1,583 protein-protein interactions within and between thegenes in the two oncogenic-ICR signatures (P<10⁻³, empirical test),consistent with participation in convergent biological processes.Applicants therefore defined the oncogenic-ICR state as a concurrentinduction of the oncogenic-ICR-up signature and repression of theoncogenic-ICR-down signature, which Applicants quantify by the overallexpression (materials and methods) of the oncogenic-ICR-up signatureminus the overall expression of the oncogenic-ICR-down signature.

Next, Applicants hypothesized that the oncogenic-ICR signatures reflectan active resistance program, rather than only a post-ICI malignant cellstate. This would be consistent with the presence of cells expressingthe program in TN patients. In particular, to resist ICI, malignantcells may not only evade the immune cells (e.g., through the repressionof MEW I and IFN-γ in oncogenic-ICR-down) but may also actively excludethe immune cells. The latter will impact the extent of CD8 T cellinfiltration, which is a known pre-treatment predictor of ICI response(6, 27). To explore this possibility, Applicants developed a data-drivenapproach that characterizes malignant cells in non-infiltrated niches ortumors (FIG. 2F). In this approach, Applicants combined single cellprofiles (irrespective of treatment status) with 473 melanoma bulkexpression profiles from TCGA. First, Applicants used the single-cellprofiles to define a T cell specific signature of 143 genes, and asignature of 332 genes that were primarily expressed by malignant cells(table S4). Then Applicants estimated the T cell infiltration level ofthe TCGA tumors based on their expression of the T cell signature(materials and methods), and identified malignant genes whose expressionwas correlated to the estimated T cell infiltration levels. Six and 20of the 332 malignant cell genes were significantly correlated oranti-correlated to the T cell infiltration level, respectively, whichApplicants termed the seed T cell exclusion (Exclusion)-down and -upmodules, respectively. However, the seed modules would neglect genesthat are expressed also by other, non-malignant cells in the tumor (asMHC I, IFN-γ). To recover these, Applicants correlated the expression ofeach gene to the expression of the seed Exclusion modules across theentire malignant single-cell profiles. This yielded the finalExclusion-up and down modules, with 101 and 134 genes, respectively(table S6).

The Exclusion-down module was enriched for antigen processing andpresentation genes (B2M, CTSB, CTSL1, HLA-B/C/F, HSPA1A, HSPA1B,P=4.19*10⁻⁷, hypergeometric test), immune modulation genes (P=3.84*10⁻⁹,as CD58 and the NFκB inhibitor, NFKBIA), and genes involved in theresponse to the Complement system (P=2.26*10⁻⁷, e.g., CD59 and C4A).CD58 KO in malignant cells was recently shown to enhance the survival ofmelanoma cells in a genome-scale CRISPR screen of melanoma/T cellco-cultures (28), and its genetic loss or epigenetic inactivation arefrequent immune evasion drivers in diffuse large B cell lymphoma (29).The Exclusion-up module included MYC itself and Myc targets(P=6.8*10⁻¹²), as well as the transcription factors SNAI2 and SOX4.

Even though the Exclusion modules were identified without consideringthe treatment status of the tumors (TN or ICR), they significantlyoverlapped the corresponding oncogenic-ICR signatures (64 and 52overlapping genes in oncogenic-ICR-up and -down, respectively, P<10⁻¹⁶,hypergeometric test, FIG. 2G,H). Both oncogenic-ICR (AUC=0.83, incross-validation) and the Exclusion signatures (AUC=0.86) robustlyclassified individual cells as TN or ICR (FIG. 2A,G). In light of thiscongruence, Applicants defined a unified resistance program (uICR-up anduICRdown) as the union of the corresponding oncogenic-ICR and Exclusionsignatures.

Importantly, there was no significant difference between the fraction ofcycling cells in ICR vs. TN tumors (P=0.696, t-test), and theoncogenic-ICR signatures were identical when identified only based onnon-cycling cells. Interestingly however, the oncogenic-ICR state wasmore pronounced in cycling cells, both within the same patient group andamong cells of the same tumor (FIG. 2B,H, FIG. 10A,B, P<10⁻¹⁶, mixedeffects model). Thus, cycling malignant cells may have induced strongerimmune evasion capacities compared to their non-cycling counterparts.Moreover, CDK4 was a member of the of the induced resistance program(uICRup). Applicants thus hypothesized that its targeted inhibitioncould shift the malignant cells to a less resistant state.

Unlike other biomarkers, such as PDL1 expression, mutational load, or Tcell infiltration levels, the immune resistance signature couldpotentially provide a basis to develop novel treatment strategies. Next,Applicants explored therapeutic strategies to overcome resistance byreversing the uICR cell state in cancer cells. As CDK4 and multiple CDKtarget were members of the of the induced resistance program (uICR-up)and as the ICR state was more pronounced in cycling cells, Applicantshypothesized that cell cycle arrest through CDK4/6 inhibition couldshift the malignant cells to a less resistant state. Additionally,CDK4/6 inhibitors could potentially increase tumor cell immunogenicityby inducing SASP, which was significantly repressed in the cancer cellsfrom the ICR tumors compared to those from the untreated ones.

To test this assumption, Applicants first analyzed a recently publisheddata set (30) in breast cancer cell lines and in vivo models and showedthat CDK4/6 inhibition through abemaciclib treatment represses the ICRstate defined by our signatures (FIG. 3A-B, FIG. 10C). Applicants foundthat the CDK4/6 inhibitor abemaciclib strongly repressed uICR-up (whichincludes CDK4) and induced uICR-down (which includes the D-cyclin,CCND3). Indeed, abemaciclib, approved for the treatment of BRCA-mutatedbreast cancer, was recently shown to trigger anti-tumor activity byinducing type III interferon production and suppressing T regulatorycells (30). Furthermore it was shown to sensitize solid tumors toanti-PDL1 in mouse models (30) in an RB-dependent manner.

To determine this effect in melanoma, Applicants identified melanomacell lines in the Cancer Cell Line Encyclopedia (CCLE) with thestrongest expression of the uICR signature, including IGR37, UACC257(both RB-sufficient) and A2058 (RB-deficient). Applicants performedscRNA-seq on these cell lines before and after treatment withabemaciclib for 1 week (FIG. 41). In both IGR37 and UACC257, Applicantssaw a decrease in the expression of the uICR state (P<3.59*10⁻³⁴,one-sided t-test). The single-cell resolution of the data revealed thatin IGR37 there was a subpopulation of cells with an exceptionally strongexpression of the uICR signature prior to the treatment with abemaciclib(FIG. 43). This population decreased from 10% before treatment (2,454cells) to 2% in the post-treatment condition (1,570 cells). In contrast,the RB-deficient cell line A2058 did not show changes in the uICRexpression, consistent with the hypothesis that this effect depends onRB-sufficiency.

Interestingly, Applicants found that DNMT1 is repressed while ERV-3 isinduced in IGR37 and UACC257 cells post-treatment. These findingssupport previous observations that CDK4/6 inhibition leads to DNMT1repression, allowing the methylation of endogenous retroviral genes(ERVs). The induction of ERVs triggers ‘viral mimicry’ and adouble-stranded RNA (dsRNA) response, which stimulates type III IFNproduction to activate IFN-stimulated genes. Interestingly, Applicantsalso find that abemaciclib induces the expression of an MITF signature(Tirosh I, et al., Science. 2016 Apr. 8; 352(6282):189-96) and of theSASP module (FIG. 42). The resistant cells, which are eradicated oraltered by abemaciclib, had the lowest expression of the MITF and SASPsignatures. While this pattern is decoupled from the expression of theMITF gene itself, it nonetheless indicates that, unlike the mechanismdescribed in breast cancer cells (30), abemaciclib might trigger SASPand cell differentiation in melanoma cells.

To explore the potential of abemaciclib to induce T cell mediatedtoxicity to tumor cells, Applicants leveraged a patient-derivedco-culture model of melanoma cells and ex-vivo expanded tumorinfiltrating lymphocytes (TILs) from a metastatic melanoma lesion.Following one week of treatment of tumor cells with abemaciclib, cellswere treated with their autologous TILs vs. control, and surviving tumorcells were submitted to scRNA-seq. The exposure to TILs reduced theexpression of the uICR signature, both in the control andabemaciclib-treated cells (P<4.91*10⁻¹³). The treatment with abemaciclibfurther intensifies these effects, such that in the abemaciclib-treatedcells there was an increase in a sensitive (ICR-low) subpopulation ofcells post-TILs (FIG. 42). These sensitive cells are also characterizedby a low expression of DNMT1, overexpression of ERV-3, and higherexpression of the MITF and SASP modules. Furthermore, Applicantsmeasured 40 human cytokines/chemokines in the conditioned media ofabemaciclib treated cancer cells (before co-culture) and found theinduction of several secreted factors (FIG. 42): macrophage inhibitionfactor (MIF), CX3CL1 a chemokine that induces migration and adhesion ofT and NK cells and is linked to clinical outcomes in immunotherapytreatment (38, 39), and CCL20, an important factor for T celldifferentiation that may enhance immunity in melanoma (40).

The relevance of the uICR as a resistance program is further supportedby several lines of evidence. First, the induced uICR is overexpressedin uveal melanoma, which resides in an immune-privileged environment andhas very low response rates to immunotherapy (31, 32), compared tocutaneous melanoma (FIG. 3D). Second, perturbations of genes from therepressed resistance program (uICR-down) in malignant melanoma cellsconferred resistance to cytotoxic CD8 T cells in a genome-wide CRISPR KOscreen (P=6.37*10⁻³, hypergeometric test). Third, malignant cells in theresistant state substantially repress interaction routes with other celltypes in the tumor (FIG. 3E), as defined by cognate pairs of interactingsurface molecules (materials and methods), including MEW I:TCR (Tcells), CD58:CD2 (T cells), and IL1RAP:IL1B (macrophages).

These results support a model, in which malignant cells from ICR tumorseither had active resistance programs prior to treatment or induced theresistance program upon ICI exposure. Because some of the malignantcells from the TN patients expressed the resistance programs (FIG. 2B,H)Applicants next tested their prognostic value in independent datasetsand cohorts. To this end, Applicants used both the full uICR and furtherfiltered/refined uICR signatures. The refined signatures include onlyuICR genes that were also co-regulated with genes whose inhibitionenhanced melanoma cell resistance to T cell mediated killing infunctional screens (28) (table S6, materials and methods); theoncogenic-ICR and Exclusion signatures show the same behavior (FIG.4E-H, FIGS. 11-13).

The uICR programs are prognostic and predictive for response in externaldata sets. First, the signatures strongly associated with survival in431 TCGA melanoma patients (who did not receive ICI, FIG. 4A, FIG. 11),even after controlling for tumor purity and T cell infiltration, a knownprognostic marker in melanoma (33, 34). Furthermore, combiningresistance signatures and T cell infiltration levels yielded asignificantly stronger association of patient survival than either alone(COX p-value=1.4*10⁻⁸, FIG. 4A, right). Other proposed mechanisms, suchas dedifferentiation of melanoma cells (35), as reflected by an MITF-lowsignature, and other malignant signatures from the single cell profiles(e.g., cell cycle and the AXL program) (13), did not show an associationto patient survival, indicating that mere variation across malignantcells is insufficient as a prognostic signature. Second, the signatureswere associated with benefit of ICI in published pre-treatment and earlyon-treatment bulk expression profiles. In a lung cancer mouse model,which was mostly free of confounding genetic variability, the uICRclearly separated anti-CTLA-4 responders from non-responders based onearly on-treatment profiles (P=3.6*10⁻⁷, one-sided t-test, FIG. 4B)(36). In bulk pre-treatment RNA-Seq data from 27 melanoma patients thatwere subsequently treated with Pembrolizumab (5), the uICR program waslower in the five complete responders, though just above statisticalsignificance (P=6.3*10⁻², FIG. 4C). In bulk pre-treatment RNA-Seq datafrom 42 melanoma patients that were subsequently treated with the CTLA-4inhibitor ipilimumab (4), the uICR program was significantly lower inthe two complete responders (P=5.2*10⁻³).

To test the predictive value of the resistance program in a largerindependent setting, Applicants assembled a validation cohort of 112patients with metastatic melanoma who underwent pretreatment biopsy andbulk RNA-Seq followed by Pembrolizumab (anti-PD-1) therapy (FIG. 1A,table S1). The cohort was collected in a different hospital and country(Germany), and samples were processed and sequenced on the same platformat the Broad Institute (materials and methods). Applicants evaluated theperformances of the malignant resistance modules in predicting anti-PD-1responses, with respect to three parameters (materials and methods): (1)progression-free survival (PFS, recorded for 104 of the 112 patients),(2) clinical benefit (CB, defined as either partial or complete responseby RECIST criteria), and (3) complete response (CR). To compare theperformance of the predictors to prior knowledge and clinically usedmarkers, Applicants assembled a set of 32 other transcriptionalsignatures, including the top hits of two ICR functional CRISPR screens(28, 37) (table S10).

Our malignant cell resistance signatures were predictive of PFS in thevalidation cohort (FIG. 4D,E, FIGS. 12 and 13). Their predictive valuewas significant even when accounting for other known predictors of ICIresponse, including inferred T cell infiltration levels and PD-L1expression (FIGS. 12E and 13E). Although cell cycle alone is notassociated with CB (COX P >0.25), filtering the cell-cycle componentfrom the uICR overexpression score (materials and methods) furtherimproved the PFS predictions (FIG. 4D, right), suggesting that a tumorICR level should be evaluated conditioning on its proliferation level.The additional predictive value of the malignant resistance signaturesbeyond T cell infiltration was significantly higher than that of othersignatures (P=3.37*10⁻⁶, Wilcoxon-ranksum test), and they were the onlyones negatively associated with PFS. Other alternative predictors wereeither not predictive or highly associated with T cell infiltrationlevels, such that they did not provide an additive predictive value onceaccounting for T cell infiltration levels (FIG. 4E).

The uICR state was overexpressed in patients with CB vs. no-CB (FIG.4F). Applicants noted however that some CB patients had highpre-treatment uICR expression and hypothesized that these patients,while experiencing an initial CB, might cease to respond quickly.Indeed, when stratifying patients with CB based on the duration of theirresponse (>12 months, <12 months but >6 months, and <6 months),Applicants found that patients with an initial CB but high uICR scorepretreatment were significantly more likely to experience subsequentprogressive disease (FIG. 4F). Indeed, patients with rapid progression,that is CB<6 months had the highest uICR score, even compared to thosewith no-CB. Consistently, the resistance signatures were most accuratein predicting patients with complete responses (P<6.31*10⁻³, one-sidedt-test, FIG. 4G, FIG. 14). In this task, they were superior to all theother alternative predictors (P=1.64*10⁻⁸, Wilcoxon ranksum test), allof which, including the clinically used markers, failed to predictcomplete response (FIG. 411).

Finally, Applicants explored intrinsic vs. acquired uICR programs in anadditional independent cohort, collected in yet another hospital(materials and methods), consisting of 90 samples from 26 patients withmetastatic melanoma who underwent both pre-treatment andpost-progression biopsies with bulk RNA-Seq, including 17 patients withon-treatment biopsy (FIG. 1A). The ICR state was induced following ICIcompared to pre-ICI lesions from the same patient (P=1.26*10⁻⁴ and 0.01,for the refined uICR and uICR-up, respectively; mixed-effect test,materials and methods). However, inter-patient variation in uICRexpression was significantly higher than intra-patient changes (P<10⁻⁸,ANOVA). This suggests that one pre-treatment sample per patient maysuffice to evaluate ICR for many patients, and that intrinsic resistancemay be more prevalent than acquired resistance, consistent with clinicalobservations (3). Notably, Applicants did not observe an induction inuICR expression following RAF/MEK-inhibition (materials and methods),indicating that the ICR state is specific to ICI therapy and not merelya marker of a generally drug resistant tumor ecosystem.

Discussion

Applicants discovered new features linked to response and resistance toimmunotherapy in metastatic melanoma with a strong prognostic andpredictive value in independent patient cohorts. T cell profiles fromICR patients reflect variability in T cell responses, which are oftendecoupled from the clinical response. In some ICR patients, T cellsmanifest substantial clonal expansions, in others higher frequency of Tcell proliferation, or a shift in the cytotoxicity/exhaustion balance.While more data is needed to distinguish between proper and insufficientT cell response to ICI, the results suggest that malignantcell-autonomous programs may be another key contributor to ICR, even inthe presence of properly activated T cells (FIG. 4I).

Malignant cell programs that suppress interactions with the tumormicroenvironment, modulate key inflammatory pathways and activatemechanisms of T cell exclusion were distinguishing features of ICRtumors. These may be jointly controlled as a single coherent resistanceprogram to confer ICR, through master regulators like Myc and CDK4/6.While these programs were initially identified in post-progressionsamples using scRNA-Seq, Applicants validated their predictive value ina pre-ICI cohort and explored their expression in matched pre/postspecimens of ICI-treated patients. The ICR signatures Applicantsidentified were superior to a comprehensive and diverse set ofalternative predictors in several ways, especially in predictingcomplete responders and patients that responded for more than 6 months.Unlike other predictors, the ICR signatures have a significantadditional predictive value beyond pre-treatment T cell infiltrationlevels, indicating that they highlight new and yet unappreciated aspectsof ICR. In light of these results, the signatures may help stratifypatients for ICI beyond currently used biomarkers.

The pathways represented in the resistance program also highlightpotential mechanistic causes of ICR that could be reversed by combiningICI with other drugs. Combination of ICI with CDK4/6 inhibitors (such asabemaciclib) may be particularly attractive in light of the findingsthat abemaciclib reverses the resistant oncogenic state and that thereare distinctions between the cell cycle programs of malignant cells andT cells.

The malignant resistance programs may be relevant in other subtypes ofmelanoma and even in other lineage-independent cancer types. Amongdifferent types of melanoma, uveal melanoma has more active resistanceprograms compared to cutaneous melanoma (FIG. 3D); across cancers, theresistance program is higher in some cancer types that are lessresponsive to immunotherapy and/or arise from immune-privileged tissues(eye, testis) and lower in some of the more responsive tissues (head andneck, kidney, skin, lung) (FIG. 15). This distinction, however, isimperfect, and additional, tumor-specific resistance programs may bediscovered by similar strategies. Our study uncovers an improved,potentially clinically applicable biomarker for patient selection,provides a rationale to examine novel mechanisms of ICR, and revealsguiding principles to further dissect and repress mechanisticunderpinnings that mediate ICI resistance.

Applicants demonstrated that cancer cell-autonomous ICR programsidentified by scRNA-Seq predict clinical response (per RECIST criteria)and progression-free survival in two independent cohorts: one ofpatients who underwent RNA-seq of matched pre-treatment and progression(ICR) specimens; and another of 112 melanoma patients with pre-treatmentRNA-seq who receive anti-PD-1 monotherapy. Applicants also validated theprognostic value of these cell programs in TCGA. Lastly, Applicantsdemonstrated that pharmacological reversal of these oncogenic cellstates can be achieved by CDK4/6-inhibition, and explored the impact ofthis treatment in melanoma at the single cell level. To determine therole of T cell exclusion from the TME as a potential mechanism of ICR,Applicants performed spatially resolved 30-plex single-cell proteinanalysis of matching FFPE specimens from 16 of the patients who alsounderwent scRNA-seq. Thus, the presented analytical platforms provide apromising approach to understanding drug resistance within preservedtumor ecosystems.

In conclusion, this study provides a high-resolution landscape ofoncogenic ICR states, identifies clinically predictive signatures, andforms a basis to develop novel therapeutic strategies that couldovercome immunotherapy resistance in melanoma.

TABLE S1 Clinical characteristics of the patients and samples in thescRNA-Seq cohort, and in the two validation cohorts. scRNA-Seq cohortTreatment Lesion Sample Cohort Age Sex Treatment group type Site Mel53Tirosh et 77 F None TN metastasis Subcutaneous al. 2016 back lesionMel58 Tirosh et 83 M Ipilimumab ICR metastasis Subcutaneous al. 2016 leglesion Mel60 Tirosh et 60 M Trametinib, ICR metastasis Spleen al. 2016ipilimumab Mel71 Tirosh et 79 M None TN metastasis Transverse al. 2016colon Mel72 Tirosh et 57 F IL-2, nivolumab, ICR metastasis Externaliliac al. 2016 ipilimumab + anti- lymph node KIR-Ab Mel74 Tirosh et 63 MNivolumab ICR metastasis Terminal al. 2016 Ileum Mel75 Tirosh et 80 MIpilimumab + ICR metastasis Subcutaneous al. 2016 nivolumab, WDVAX leglesion Mel78 Tirosh et 73 M WDVAX, ICR metastasis Small bowel al. 2016ipilimumab + nivolumab Mel79 Tirosh et 74 M None TN metastasis Axillarylymph al. 2016 node Mel80 Tirosh et 86 F None TN metastasis Axillarylymph al. 2016 node Mel81 Tirosh et 43 F None TN metastasis Axillarylymph al. 2016 node Mel82 Tirosh et 73 F None TN metastasis Axillarylymph al. 2016 node Mel84 Tirosh et 67 M None TN primary Acral primaryal. 2016 tumor Mel88 Tirosh et 54 F Tremelimumab + ICR metastasisCutanoues al. 2016 MEDI3617 met Mel89 Tirosh et 67 M None TN metastasisAxillary lymph al. 2016 node Mel94 Tirosh et 54 F IFN, ipilimumab + ICRmetastasis Iliac lymph al. 2016 nivolumab node Mel126 Additional 63 MIpilimumab, ICR metastasis Soft tissue nivolumab Mel04.3 Additional 81 MIpilimumab CB metastasis Skin Mel110 Additional 74 M ipilimumab + ICRmetastasis R adrenal angiopoietin 2 metastasis inhibitor, Temezlolamide,Pembrolizumab Mel121.1 Additional 74 M S/p Pembrolizumab ICR metastasisSkin Mel106 Additional 67 M Prior treatment: ICR metastasis Necrotic Lnivolumab + axillary lymph ipilimumab nodes Mel75.1 Additional 81 MIpilimumab + ICR metastasis Soft tissue nivolumab, WDVAX, PembrolizumabMel98 Additional 47 F S/p IFN, s/p ICR metastasis L thigh softipilimumab + GMCSF tissue metastasis Mel102 Additional 72 F S/pnivolumab + ICR metastasis Fragmented ipilimumab pieces of (R) adrenalgland metastasis Mel129PA Additional 63 M None TN primary Skin tumorMel129PB Additional 63 M None TN primary Skin tumor Mel116 Additional 85M None TN metastasis Lymph node Mel103 Additional 58 M None TNmetastasis Lymph node Mel105 Additional 77 M None TN primary Skin tumorMel112 Additional 76 M None TN metastasis Bulky (L) axillary metastasisMel194 Additional 68 M Nivolumab + ICR metastasis L anterior lirilumab(anti-kit), shoulder Nivolumab, subcutaneous Ipilimumab, Pan-RAF-inhibitor, Pembrolizumab Mel478 Additional 54 F None TN metastasisTransanal rectal mass Mel128 Additional 37 M None TN metastasis Lymphnode Number of therapies prior to Cohort 1 Sex, n anti-PD-1 therapy, nRECIST category Patients 1-112 Female, 49 No prior treatment, 49 PD, 49Male, 56 1, 34 SD, 13 n/a, 7 2, 14 PR, 25 3, 6 CR, 14 7, 2 n/a, 11 n/a,7 Number of samples Cohort 2 per patient, n Patients 1-26 2, 10 90samples 3, 8 4, 3 6, 2 7, 2 8, 1

TABLE S2 Table S2. Quality measures of scRNA-Seq experiments. Median no.of Median no. of No. of TN No. of ICR Total no. of Cell type detectedgenes aligned reads cells cells cells B. cell 3774 164400 463 355 818CAF 5518 357423 61 45 106 Endothelial 5057 304326 87 17 104 cellMacrophage 5670 654482 161 259 420 Mal 5482 335563 1193 825 2018 NK 3909147376 44 48 92 CD4 T cell 4036 220614 420 436 856 CD8 T cell 4064264494 720 1039 1759 T cell 3827 234410 298 408 706 (unresolved) Lowquality 732 24991 1386 1551 2937 cell ? 3433 221421 183 124 307 Allcells 3559 377141 5016 5107 10123

TABLE S4 Table S4. Cell type signatures that were derived from theanalysis of the scRNA-seq data (see section Data-driven signatures ofspecific cell-types). Endo- B thelial Macro- Malignant NK CD4 cell CAFcell phage cell cell T cell T cell ADAM19 ABI3BP A2M ACP5 AATF CCL4 AAK1AQP3 ADAM28 ACTA2 ADAM15 ACSL1 ACN9 CD244 ACAP1 CCR4 AFF3 ADAM12 ADAMTS9ADAMDEC1 ACSL3 CST7 AKNA CD28 BANK1 ADAMTS2 ADCY4 ADAP2 AHCY CTSWAPOBEC3G CD4 BCL11A ANTXR1 AFAP1L1 ADORA3 AIF1L GNLY ARAP2 CD40LG BIRC3ASPN APLNR ADPGK AK2 GZMA ARHGEF1 CD5 BLK C1S AQP1 AIF1 ALX1 GZMB ASB2DGKA BLNK CALD1 ARHGEF15 AKR1A1 AMD1 HOPX ATHL1 F5 BTLA CCDC80 CALCRLALDH2 ANKRD20A12P ID2 BCL11B FAAH2 CCR6 CD248 CCL14 ALDH3B1 ANKRD54IL2RB BTN3A2 FOXP3 CCR7 CDH11 CD200 AMICA1 AP1S2 KLRB1 CBLB ICOS CD19CERCAM CD34 ANKRD22 APOA1BP KLRC1 CCL4 IL6R CD1C COL12A1 CD93 AP1B1APOC2 KLRD1 CCL5 IL7R CD22 COL14A1 CDH5 AQP9 APOD KLRF1 CD2 PASK CD24COL1A1 CFI ATF5 APOE KLRK1 CD247 PBXIP1 CD37 COL1A2 CLDN5 ATG3 ATP1A1NCAM1 CD27 SLAMF1 CD52 COL3A1 CLEC14A ATG7 ATP1B1 NKG7 CD28 SPOCK2 CD79ACOL5A1 COL15A1 ATP6V0B ATP5C1 PRF1 CD3D TCF7 CD79B COL5A2 COL4A1ATP6V0D1 ATP5G1 PTGDR CD3E TNFSF8 CD82 COL6A1 COL4A2 ATP6V1B2 ATP5G2SH2D1B CD3G CHMP7 COL6A2 CRIP2 BCL2A1 ATP6V0E2 XCL1 CD5 CIITA COL6A3CXorf36 BID ATP6V1C1 CD6 CLEC17A COL8A1 CYYR1 BLVRA ATP6V1E1 CD7 CNR2CREB3L1 DARC BLVRB ATP6V1G1 CD8A COL19A1 CXCL14 DCHS1 C11orf75 AZGP1CD8B COL4A3 CYBRD1 DOCK6 C15orf48 BAIAP2 CD96 CR2 DCN DOCK9 C19orf38BANCR CDC42SE2 CXCR5 DPT DYSF C1orf162 BCAN CELF2 ELK2AP EFEMP2 ECE1C1QA BCAS3 CLEC2D FAIM3 FBLN1 ECSCR C1QB BCHE CNOT6L FAM129C FBLN5 EGFL7C1QC BIRC7 CST7 FCER2 FGF7 ELK3 C2 BZW2 CTLA4 FCRL1 GPR176 ELTD1 C3AR1C10orf90 CTSW FCRL2 HSPB6 EMCN C5AR1 C11orf31 CXCL13 FCRL5 INHBA ENGC9orf72 C12orf76 CXCR3 FCRLA ISLR EPAS1 CAPG C17orf89 CXCR6 HLA-DOBITGA11 EPHB4 CARD9 C1orf43 DEF6 HLA-DQA2 LOX ERG CASP1 C1orf85 DENND2DHVCN1 LPAR1 ESAM CCR1 C4orf48 DGKA IGLL1 LTBP2 FGD5 CCR2 CA14 DTHD1IGLL3P LUM FKBP1A CD14 CA8 DUSP2 IGLL5 MAP1A FLT4 CD163 CACYBP EMB IRF8MEG3 GALNT18 CD274 CAPN3 EVL KBTBD8 MIR100HG GPR116 CD300C CBX3 FASLGKIAA0125 MRC2 HERC2P2 CD300E CCDC47 FYN KIAA0226L MXRA8 HSPG2 CD300LBCCND1 GATA3 LOC283663 MYL9 HYAL2 CD300LF CCT2 GPR171 LRMP NID2 ICA1CD302 CCT3 GPR174 LTB OLFML3 ID1 CD33 CCT6A GPRIN3 MS4A1 PALLD IL3RA CD4CCT8 GRAP2 NAPSB PCDH18 ITGB4 CD68 CDH19 GZMA NCOA3 PCOLCE KDR CD80 CDH3GZMB P2RX5 PDGFRA LAMA5 CD86 CDK2 GZMH PAX5 PDGFRB LDB2 CECR1 CHCHD6GZMK PLEKHF2 PDGFRL LOC100505495 CFP CITED1 GZMM PNOC PLAC9 MALL CLEC10ACLCN7 HNRNPA1P10 POLD4 PODN MMRN1 CLEC12A CLNS1A ICOS POU2AF1 PRRX1MMRN2 CLEC4A CMC2 IFNG POU2F2 RARRES2 MYCT1 CLEC4E COA6 IKZF1 QRSL1 RCN3NOS3 CLEC5A COX5B IKZF3 RALGPS2 SDC2 NOTCH4 CLEC7A COX7A2 IL12RB1 RPL13SFRP2 NPDC1 CMKLR1 COX7C IL2RB RPS20 SLIT3 PALMD CNPY3 CRYL1 IL2RG RPS23SMOC2 PCDH17 COTL1 CSAG1 IL32 SEL1L3 SPOCK1 PDE2A CPVL CSAG2 IL7R SELLSULF1 PDLIM1 CREG1 CSAG3 INPP4B SMIM14 SVEP1 PECAM1 CSF1R CSPG4 IPCEF1SNX29 TAGLN PLVAP CSF2RA CTNNB1 ITGAL SNX29P1 THBS2 PLXND1 CSF3R CYC1ITK SPIB THY1 PODXL CSTA CYP27A1 JAK3 ST6GAL1 TMEM119 PRCP CTSB DAAM2JAKMIP1 STAG3 TPM1 PREX2 CTSC DANCR KLRC4 STAP1 TPM2 PTPRB CTSD DAP3KLRD1 TCL1A PVRL2 CTSH DCT KLRK1 TLR10 RAMP2 CTSS DCXR LAG3 TMEM154RAMP3 CXCL10 DDIT3 LAT TNFRSF13B RHOJ CXCL16 DDT LCK VPREB3 ROBO4 CXCL9DFNB31 LEPROTL1 WDFY4 S1PR1 CXCR2P1 DLL3 LIME1 ZCCHC7 SDPR CYBB DNAH14LOC100130231 SELP CYP2S1 DNAJA4 MAP4K1 SHROOM4 DAPK1 DSCR8 MIAT SLCO2A1DHRS9 DUSP4 NELL2 SMAD1 DMXL2 EDNRB NKG7 STOM DNAJC5B EIF3C NLRC3 SYNPOEBI3 EIF3D NLRC5 TAOK2 EMR2 EIF3E OXNAD1 TEK EPSTI1 EIF3H PAG1 TENC1F13A1 EIF3L PARP8 TGFBR2 FAM157B ENO1 PCED1B TGM2 FAM26F ENO2 PCED1B-AS1THBD FBP1 ENTHD1 PDCD1 TIE1 FCER1G ENTPD6 PIK3IP1 TM4SF1 FCGR1A ERBB3PIM2 TM4SF18 FCGR1B ESRP1 PIP4K2A TMEM255B FCGR1C ETV4 PPP2R5C TSPAN18FCGR2A ETV5 PRDM1 TSPAN7 FCGR2C EXOSC4 PRF1 VWF FCN1 EXTL1 PRKCQ ZNF385DFGL2 FAHD2B PSTPIP1 FOLR2 FAM103A1 PTPN22 FPR1 FAM178B PTPN7 FPR2 FANCLPVRIG FPR3 FARP2 PYHIN1 FTH1 FASN RAB27A FTL FBXO32 RAPGEF6 FUCA1 FBXO7RARRES3 FUOM FDFT1 RASAL3 GABARAP FKBP4 RASGRP1 GATM FMN1 RGS1 GBP1FXYD3 RHOF GCA GALE RNF213 GK GAPDH RUNX3 GLA GAPDHS SCML4 GLRX GAS2L3SEMA4D GLUL GAS5 1-Sep GM2A GAS7 SH2D1A GNA15 GDF15 SH2D2A GPBAR1 GJB1SIRPG GPR34 GPATCH4 SIT1 GPR84 GPM6B SKAP1 GPX1 GPNMB SLA2 GRN GPR137BSPATA13 HCAR2 GPR143 SPN HCAR3 GPS1 SPOCK2 HCK GSTP1 STAT4 HK2 GYG2SYTL3 HK3 H2AFZ TARP HLA-DMA HAX1 TBC1D10C HLA-DMB HIST1H2BD TC2NHLA-DPA1 HIST3H2A TESPA1 HLA-DPB1 HMG20B THEMIS HLA-DPB2 HMGA1 TIGITHLA-DRA HPGD TNFAIP3 HLA-DRB1 HPS4 TNFRSF9 HLA-DRB5 HPS5 TOX HLA-DRB6HSP90AA1 TRAF1 HMOX1 HSP90AB1 TRAT1 HSPA6 HSPA9 TTC39C HSPA7 HSPD1UBASH3A IFI30 HSPE1 WIPF1 IFNGR1 IGSF11 ZAP70 IFNGR2 IGSF3 ZC3HAV1IGFLR1 IGSF8 IGSF6 1NPP5F IL18 IRF4 IL1B ISYNA1 IL1RN KCNJ13 IL4I1 LAGE3IL8 LDHB IRF5 LDLRAD3 KCNMA1 LEF1-AS1 KYNU LHFPL3-AS1 LAIR1 LINC00473LGALS2 LINC00518 LGMN LINC00673 LILRA1 LOC100126784 LILRA2 LOC100127888LILRA3 LOC100130370 LILRA5 LOC100133445 LILRA6 LOC100505865 LILRB1LOC146481 LILRB2 LOC340357 LILRB3 LONP2 LILRB4 LOXL4 LILRB5 LZTS1 LIPAMAGEA1 LOC729737 MAGEA12 LRRC25 MAGEA2 LST1 MAGEA2B LTA4H MAGEA3 LYZMAGEA4 MAFB MAGEA6 MAN2B1 MAGEC1 MARCO MDH1 MFSD1 MDH2 MILR1 MFI2 MNDAMFSD12 MOB1A MIA MPEG1 MIF MPP1 MITF MS4A4A MLANA MS4A6A MLPH MS4A7 MOKMSR1 MRPS21 MTMR14 MRPS25 MYD88 MRPS26 NAAA MRPS6 NADK MSI2 NAGA MXI1NAGK MYO10 NAIP NAV2 NCF2 NDUFA4 NCF4 NDUFB9 NCOA4 NDUFS2 NFAM1 NEDD4LNINJ1 NELFCD NLRC4 NHP2 NLRP3 NME1 NMI NOP58 NPC2 NPM1 NPL NSG1 OAS1NT5C3 OAZ1 NT5DC3 OLR1 OSTM1 OSCAR PACSIN2 P2RY12 PAGE5 P2RY13 PAICSPAK1 PAX3 PCK2 PEBP1 PILRA PEG10 PLA2G7 PFDN2 PLAUR PHB PLBD1 PHLDA1PLEKHO1 PIGY PLIN2 PIR PPT1 PLEKHB1 PRAM1 PLP1 PRKCD PMEL PSAP POLR2FPTAFR PPIL1 PYCARD PRAME RAB20 PSMB4 RASSF4 PSMD4 RBM47 PUF60 RELT PYGBRGS10 PYURF RGS18 QDPR RGS19 RAB17 RGS2 RAB38 RHBDF2 RAN RILPL2 RAP1GAPRIPK2 RGS20 RNASE6 ROPN1 RNASET2 ROPN1B RNF13 RPL38 RNF130 RPS6KA5RNF144B RSL1D1 RTN1 RTKN S100A8 S100A1 S100A9 S100B SAMHD1 SCD SAT1 SDC3SDS SEC11C SECTM1 SEMA3B SEMA4A SERPINA3 SERPINA1 SERPINE2 SIGLEC1 SGCDSIGLEC5 SGK1 SIGLEC9 SHC4 SIRPB1 SLC19A2 SIRPB2 SLC24A5 SLAMF8 SLC25A13SLC11A1 SLC25A4 SLC15A3 SLC26A2 SLC1A3 SLC3A2 SLC29A3 SLC45A2 SLC31A2SLC5A3 SLC7A7 SLC6A15 SLCO2B1 SLC7A5 SMPDL3A SNCA SNX10 SNHG16 SOD2SNHG6 SPI1 SNRPC SPINT2 SNRPD1 STAT1 SNRPE STX11 SOD1 TBXAS1 SORD TGFBISORT1 THEMIS2 SOX10 TIFAB SPCS1 TLR1 SPRY4 TLR2 ST13 TLR5 ST3GAL4 TLR8ST3GAL6 TMEM106A ST3GAL6-AS1 TMEM176A ST6GALNAC2 TMEM176B STIP1 TMEM37STK32A TNFAIP2 STMN1 TNFAIP8L2 STX7 TNFSF13 STXBP1 TNFSF13B SYNGR1 TPP1TBC1D7 TREM1 TBCA TREM2 TEX2 TWF2 TFAP2A TYMP TFAP2C TYROBP TMEM147UBE2D1 TMEM14B VAMP8 TMEM177 VMO1 TMEM251 VSIG4 TMX4 ZNF385A TNFRSF21TOM1L1 TOMM20 TOMM22 TOMM6 TOMM7 TOP1MT TRIB2 TRIM2 TRIM63 TRIML2TRMT112 TSNAX TTLL4 TTYH2 TUBB2B TUBB4A TYR TYRP1 UBL3 UQCRH UTP18 VAT1VDAC1 VPS72 WBSCR22 XAGE1A XAGE1B XAGE1C XAGE1D XAGE1E XRCC6 XYLBZCCHC17 ZFAS1 ZFP106 ZNF280B ZNF330 ZNF692 B CD8 Lympho- Stroma cell Tcell Immune cell cyte cell ADAM19 AKAP5 AAK1 HLA-DRB6 AAK1 A2M ADAM28APOBEC3C ACAP1 HMHA1 ACAP1 ABI3BP AFF3 APOBEC3G ACP5 HMOX1 ADAM19 ACTA2BANK1 ARHGAP9 ACSL1 HNRNPA1P10 ADAM28 ADAM12 BCL11A ATP8A1 ADAM19 HOPXAFF3 ADAM15 BIRC3 BTN3A1 ADAM28 HSH2D AKAP5 ADAMTS2 BLK CBLB ADAMDEC1HSPA6 AKNA ADAMTS9 BLNK CCL4 ADAP2 HSPA7 ANKRD44 ADCY4 BTLA CCL4L1ADORA3 HVCN1 APOBEC3C AFAP1L1 CCR6 CCL4L2 ADPGK ICOS APOBEC3D ANTXR1CCR7 CCL5 AFF3 ID2 APOBEC3G APLNR CD19 CD27 AIF1 IFI30 AQP3 APP CD1C CD7AKAP5 IFNG ARAP2 AQP1 CD22 CD8A AKNA IFNGR1 ARHGAP15 ARHGEF15 CD24 CD8BAKR1A1 IFNGR2 ARHGAP9 ASPN CD37 CD96 ALDH2 IGFLR1 ARHGEF1 BGN CD52CLEC2D ALDH3B1 IGLL1 ASB2 C1R CD79A CRTAM ALOX5 IGLL3P ATHL1 C1S CD79BCST7 ALOX5AP IGLL5 ATP2A3 CALCRL CD82 CTSW AMICA1 IGSF6 ATP8A1 CALD1CHMP7 CXCL13 ANKRD22 IKZF1 BANK1 CCDC80 CIITA CXCR6 ANKRD44 IKZF3 BCL11ACCL14 CLEC17A DTHD1 AOAH IL10RA BCL11B CD200 CNR2 DUSP2 AP1B1 IL12RB1BIRC3 CD248 COL19A1 EOMES APOBEC3C IL16 BLK CD34 COL4A3 FASLG APOBEC3DIL18 BLNK CD93 CR2 FYN APOBEC3G IL1B BTLA CDH11 CXCR5 GPR171 AQP3 IL1RNBTN3A1 CDH5 ELK2AP GZMA AQP9 IL2RB BTN3A2 CERCAM FAIM3 GZMB ARAP2 IL2RGC16orf54 CFI FAM129C GZMH ARHGAP15 IL32 CBLB CLDN5 FCER2 GZMK ARHGAP30IL4I1 CCL4 CLEC14A FCRL1 ID2 ARHGAP4 IL6R CCL4L1 COL12A1 FCRL2 IFNGARHGAP9 IL7R CCL4L2 COL14A1 FCRL5 IKZF3 ARHGDIB IL8 CCL5 COL15A1 FCRLAIL2RB ARHGEF1 INPP4B CCR4 COL1A1 HLA-DOB ITGA4 ARPC3 INPP5D CCR6 COL1A2HLA-DQA2 ITGB7 ARRB2 IPCEF1 CCR7 COL3A1 HVCN1 JAKMIP1 ASB2 IRF5 CD19COL4A1 IGLL1 KIR2DL4 ATF5 IRF8 CD1C COL4A2 IGLL3P KLRC1 ATG3 ISG20 CD2COL5A1 IGLL5 KLRC2 ATG7 ITGA4 CD22 COL5A2 IRF8 KLRC3 ATHL1 ITGAL CD24COL6A1 KBTBD8 KLRC4 ATP2A3 ITGAM CD244 COL6A2 KIAA0125 KLRC4-KLRK1ATP6V0B ITGAX CD247 COL6A3 KIAA0226L KLRD1 ATP6V0D1 ITGB2 CD27 COL8A1LOC283663 KLRK1 ATP6V1B2 ITGB7 CD28 CREB3L1 LRMP LAG3 ATP8A1 ITK CD37CRIP2 LTB LOC100506776 BANK1 JAK3 CD3D CXCL14 MS4A1 LYST BCL11A JAKMIP1CD3E CXorf36 NAPSB MIR155HG BCL11B KBTBD8 CD3G CYBRD1 NCOA3 NELL2 BCL2A1KCNMA1 CD4 CYYR1 P2RX5 NKG7 BID KIAA0125 CD40LG DARC PAX5 OASL BIN2KIAA0226L CD5 DCHS1 PLEKHF2 PARP8 BIRC3 KIR2DL4 CD52 DCN PNOC PDCD1 BLKKLRB1 CD6 DOCK6 POLD4 PIP4K2A BLNK KLRC1 CD69 DOCK9 POU2AF1 PRF1 BLVRAKLRC2 CD7 DPT POU2F2 PRKCH BLVRB KLRC3 CD79A DYSF QRSL1 PSTPIP1 BTKKLRC4 CD79B ECE1 RALGPS2 PTPN22 BTLA KLRC4-KLRK1 CD82 ECSCR RPL13 PVRIGBTN3A1 KLRD1 CD8A EFEMP2 RPS20 PYHIN1 BTN3A2 KLRF1 CD8B EGFL7 RPS23RAB27A C11orf75 KLRK1 CD96 EHD2 SEL1L3 RARRES3 C15orf48 KYNU CDC42SE2ELK3 SELL RUNX3 C16orf54 LAG3 CELF2 ELTD1 SMIM14 SAMD3 C19orf38 LAIR1CHMP7 EMCN SNX29 SH2D1A C1orf162 LAPTM5 CIITA ENG SNX29P1 SLA2 C1QA LATCLEC17A EPAS1 SPIB SLAMF6 C1QB LAT2 CLEC2D EPHB4 ST6GAL1 SYTL3 C1QC LBHCNOT6L ERG STAG3 TARP C2 LCK CNR2 ESAM STAP1 THEMIS C3AR1 LCP1 COL19A1FBLN1 TCL1A TIGIT C5AR1 LCP2 COL4A3 FBLN5 TLR10 TNFRSF9 C9orf72 LEPROTL1CORO1A FBN1 TMEM154 TNIP3 CAPG LGALS2 CR2 FGD5 TNFRSF13B TOX CARD9 LGMNCRTAM FGF7 VPREB3 TTC24 CASP1 LILRA1 CST7 FKBP1A WDFY4 WIPF1 CBLB LILRA2CTLA4 FLT4 ZCCHC7 XCL1 CCL3 LILRA3 CTSW FSTL1 XCL2 CCL4 LILRA5 CXCL13GALNT18 CCL4L1 LILRA6 CXCR3 GNG11 CCL4L2 LILRB1 CXCR4 GPR116 CCL5 LILRB2CXCR5 GPR176 CCR1 LILRB3 CXCR6 HERC2P2 CCR2 LILRB4 CYFIP2 HSPB6 CCR4LILRB5 CYTIP HSPG2 CCR6 LIMD2 DEF6 HYAL2 CCR7 LIME1 DENND2D ICA1 CD14LIPA DGKA ID1 CD163 LITAF DTHD1 ID3 CD19 LOC100130231 DUSP2 IFITM3 CD1CLOC100506776 ELK2AP IGFBP4 CD2 LOC283663 EMB IGFBP7 CD22 LOC729737 EOMESIL3RA CD24 LPXN EVL INHBA CD244 LRMP EZR ISLR CD247 LRRC25 F5 ITGA11CD27 LSP1 FAAH2 ITGA5 CD274 LST1 FAIM3 ITGB4 CD28 LTA4H FAM129C KDRCD300A LTB FAM65B LAMA5 CD300C LY86 FASLG LAMB1 CD300E LY9 FCER2 LDB2CD300LB LYN FCRL1 LOC100505495 CD300LF LYST FCRL2 LOX CD302 LYZ FCRL3LPAR1 CD33 M6PR FCRL5 LTBP2 CD37 MAFB FCRLA LUM CD38 MAN2B1 FOXP3 MALLCD3D MAP4K1 FYB MAP1A CD3E 1-Mar FYN MEG3 CD3G MARCO GATA3 MIR100HG CD4MFSD1 GNLY MMP2 CD40LG MIAT GPR171 MMRN1 CD48 MILR1 GPR174 MMRN2 CD5MIR155HG GPRIN3 MRC2 CD52 MNDA GRAP2 MXRA8 CD53 MOB1A GZMA MYCT1 CD6MPEG1 GZMB MYL9 CD68 MPP1 GZMH NFIB CD69 MS4A1 GZMK NID2 CD7 MS4A4A GZMMNNMT CD72 MS4A6A HLA-DOB NOS3 CD74 MS4A7 HLA-DQA2 NOTCH4 CD79A MSR1HMHA1 NPDC1 CD79B MTMR14 HNRNPA1P10 OLFML3 CD80 MYD88 HOPX PALLD CD82MYO1F HSH2D PALMD CD83 NAAA HVCN1 PCDH17 CD84 NADK ICOS PCDH18 CD86 NAGAID2 PCOLCE CD8A NAGK IFNG PDE2A CD8B NAIP IGLL1 PDGFRA CD96 NAPSB IGLL3PPDGFRB CD97 NCAM1 IGLL5 PDGFRL CDC42SE2 NCF1 IKZF1 PDLIM1 CECR1 NCF1BIKZF3 PECAM1 CELF2 NCF1C IL12RB1 PLAC9 CFP NCF2 IL16 PLVAP CHMP7 NCF4IL2RB PLXND1 CIITA NCKAP1L IL2RG PODN CLEC10A NCOA3 IL32 PODXL CLEC12ANCOA4 IL6R PPIC CLEC17A NELL2 IL7R PRCP CLEC2D NFAM1 INPP4B PREX2 CLEC4ANINJ1 IPCEF1 PRRX1 CLEC4E NKG7 IRF8 PTPRB CLEC5A NLRC3 ISG20 PTRF CLEC7ANLRC4 ITGA4 PVRL2 CMKLR1 NLRC5 ITGAL PXDN CNOT6L NLRP3 ITGB7 RAMP2 CNPY3NMI ITK RAMP3 CNR2 NPC2 JAK3 RARRES2 COL19A1 NPL JAKMIP1 RCN3 COL4A3OAS1 KBTBD8 RHOJ CORO1A OASL KIAA0125 ROBO4 COTL1 OAZ1 KIAA0226L S1PR1CPVL OLR1 KIR2DL4 SDC2 CR2 OSCAR KLRB1 SDPR CREG1 OXNAD1 KLRC1 SELPCRTAM P2RX5 KLRC2 SFRP2 CSF1R P2RY12 KLRC3 SHROOM4 CSF2RA P2RY13 KLRC4SLCO2A1 CSF3R PAG1 KLRC4-KLRK1 SLIT3 CST7 PAK1 KLRD1 SMAD1 CSTA PARP15KLRF1 SMOC2 CTLA4 PARP8 KLRK1 SPARC CTSB PARVG LAG3 SPARCL1 CTSC PASKLAT SPOCK1 CTSD PAX5 LBH STOM CTSH PBXIP1 LCK SULF1 CTSS PCED1B LEPROTL1SVEP1 CTSW PCED1B-AS1 LIMD2 SYNPO CXCL10 PCK2 LIME1 TAGLN CXCL13 PDCD1LOC100130231 TAOK2 CXCL16 PIK3AP1 LOC100506776 TEK CXCL9 PIK3IP1LOC283663 TENC1 CXCR2P1 PIK3R5 LRMP TGFBR2 CXCR3 PILRA LTB TGM2 CXCR4PIM2 LY9 THBD CXCR5 PION LYST THBS2 CXCR6 PIP4K2A MAP4K1 THY1 CYBAPLA2G7 MIAT TIE1 CYBB PLAC8 MIR155HG TM4SF1 CYFIP2 PLAUR MS4A1 TM4SF18CYP2S1 PLBD1 NAPSB TMEM119 CYTH4 PLCB2 NCAM1 TMEM255B CYTIP PLEK NCOA3TPM1 DAPK1 PLEKHA2 NELL2 TPM2 DAPP1 PLEKHF2 NKG7 TSPAN18 DEF6 PLEKHO1NLRC3 TSPAN7 DENND2D PLIN2 NLRC5 VWF DGKA PNOC OASL ZNF385D DHRS9 POLD4OXNAD1 DMXL2 POU2AF1 P2RX5 DNAJC5B POU2F2 PAG1 DOCK2 PPM1K PARP15 DOCK8PPP2R5C PARP8 DOK2 PPT1 PASK DOK3 PRAM1 PAX5 DTHD1 PRDM1 PBXIP1 DUSP2PRF1 PCED1B EBI3 PRKCB PCED1B-AS1 ELK2AP PRKCD PDCD1 EMB PRKCH PIK3IP1EMR2 PRKCQ PIM2 EOMES PSAP PIP4K2A EPSTI1 PSMB10 PLAC8 EVI2A PSTPIP1PLEKHA2 EVI2B PTAFR PLEKHF2 EVL PTGDR PNOC EZR PTK2B POLD4 F13A1 PTPN22POU2AF1 F5 PTPN6 POU2F2 FAAH2 PTPN7 PPM1K FAIM3 PTPRC PPP2R5C FAM105APTPRCAP PRDM1 FAM129C PVRIG PRF1 FAM157B PYCARD PRKCH FAM26F PYHIN1PRKCQ FAM49B QRSL1 PSTPIP1 FAM65B RAB20 PTGDR FASLG RAB27A PTPN22 FBP1RAC2 PTPN7 FCER1G RALGPS2 PTPRC FCER2 RAPGEF6 PTPRCAP FCGR1A RARRES3PVRIG FCGR1B RASAL3 PYHIN1 FCGR1C RASGRP1 QRSL1 FCGR2A RASSF4 RAB27AFCGR2C RASSF5 RAC2 FCGR3A RBM47 RALGPS2 FCGR3B RCSD1 RAPGEF6 FCN1 RELTRARRES3 FCRL1 RGS1 RASAL3 FCRL2 RGS10 RASGRP1 FCRL3 RGS18 RGS1 FCRL5RGS19 RHOF FCRLA RGS2 RHOH FERMT3 RHBDF2 RNF213 FGD2 RHOF RPL13 FGD3RHOG RPS20 FGL2 RHOH RPS23 FGR RILPL2 RUNX3 FOLR2 RIPK2 SAMD3 FOXP3RNASE6 SCML4 FPR1 RNASET2 SEL1L3 FPR2 RNF13 SELL FPR3 RNF130 SEMA4D FTH1RNF144B 1-Sep FTL RNF213 SH2D1A FUCA1 RPL13 SH2D1B FUOM RPS20 SH2D2A FYBRPS23 SIRPG FYN RPS6KA1 SIT1 GABARAP RTN1 SKAP1 GATA3 RUNX3 SLA2 GATMS100A8 SLAMF1 GBP1 S100A9 SLAMF6 GBP5 SAMD3 SMIM14 GCA SAMHD1 SNX29 GKSAMSN1 SNX29P1 GLA SASH3 SP140 GLRX SAT1 SPATA13 GLUL SCIMP SPIB GM2ASCML4 SPN GNA15 SDS SPOCK2 GNLY SECTM1 ST6GAL1 GPBAR1 SEL1L3 STAG3GPR171 SELL STAP1 GPR174 SELPLG STAT4 GPR183 SEMA4A STK17B GPR34 SEMA4DSTK4 GPR84 1-Sep SYTL3 GPRIN3 SERPINA1 TARP GPSM3 SH2D1A TBC1D10C GPX1SH2D1B TC2N GRAP2 SH2D2A TCF7 GRB2 SIGLEC1 TCL1A GRN SIGLEC14 TESPA1GZMA SIGLEC5 THEMIS GZMB SIGLEC7 TIGIT GZMH SIGLEC9 TLR10 GZMK SIRPB1TMC8 GZMM SIRPB2 TMEM154 HAVCR2 SIRPG TNFAIP3 HCAR2 SIT1 TNFRSF13B HCAR3SKAP1 TNFRSF9 HCK SLA TNFSF8 HCLS1 SLA2 TNIP3 HCST SLAMF1 TOX HK2 SLAMF6TRAF1 HK3 SLAMF7 TRAF3IP3 HLA-DMA SLAMF8 TRAT1 HLA-DMB SLC11A1 TSC22D3HLA-DOB SLC15A3 TTC24 HLA-DPA1 SLC1A3 TTC39C HLA-DPB1 SLC29A3 UBASH3AHLA-DPB2 SLC31A2 VPREB3 HLA-DQA1 SLC7A7 WDFY4 HLA-DQA2 SLCO2B1 WIPF1HLA-DQB1 SMAP2 XCL1 HLA-DQB2 SMIM14 XCL2 HLA-DRA SMPDL3A ZAP70 HLA-DRB1SNX10 ZC3HAV1 HLA-DRB5 SNX20 ZCCHC7 SNX29 TMEM176A SNX29P1 TMEM176B SOD2TMEM37 SP140 TNFAIP2 SPATA13 TNFAIP3 SPI1 TNFAIP8 SPIB TNFAIP8L2 SPINT2TNFRSF13B SPN TNFRSF9 SPOCK2 TNFSF13 SRGN TNFSF13B ST6GAL1 TNFSF8 STAG3TNIP3 STAP1 TOX STAT1 TPP1 STAT4 TRAF1 STK17B TRAF3IP3 STK4 TRAT1 STX11TREM1 STXBP2 TREM2 SYK TSC22D3 SYTL3 TTC24 TAGAP TTC39C TARP TWF2TBC1D10C TYMP TBXAS1 TYROBP TC2N UBASH3A TCF7 UBE2D1 TCL1A UCP2 TESPA1VAMP8 TGFBI VAV1 THEMIS VMO1 THEMIS2 VPREB3 TIFAB VSIG4 TIGIT WDFY4 TLR1WIPF1 TLR10 XCL1 TLR2 XCL2 TLR5 ZAP70 TLR8 ZC3HAV1 TMC8 ZCCHC7 TMEM106AZNF385A TMEM154

TABLE S5 Table S5. The ICR signatures of the different immune celltypes: B-cells, macrophages, CD4 and CD8 T cells. CD8-T-cell-CD8-T-cell- macrophage- macrophage- B-cell- B-cell- CD4-T-cell-CD4-T-cell- up down up down up down up down CEP19 ACP5 APOL1 A2M C6orf62MTRNR2L1 PRDM1 CHI3L2 EXO5 AKNA CD274 ADAP2 CDC42 MTRNR2L10 RPL13AFAM153C BTN3A2 CSTB ADORA3 CNN2 MTRNR2L3 FCRL6 CCDC141 DCN ARL4C FOXP1MTRNR2L4 GBP2 CD27 HLA-DPB2 ASPH FYB RGS2 GBP5 CDC42SE1 HLA-DQA1 BCAT1GRB2 HSPA1B DDIT4 HLA-G C11orf31 IER2 FAU HSPA8 C3 IRF1 FKBP5 HSPB1C3AR1 KLRK1 GPR56 IL18BP C6orf62 LDHA HAVCR2 TMEM176A CAPN2 LOC100506083HLA-B UBD CD200R1 MBOAT1 HLA-C CD28 SEMA4D HLA-F CD9 SIRT3 IL6ST CD99SPDYE2 ITGA4 COMT SPDYE2L KIAA1551 CREM STAT1 KLF12 CRTAP STOM MIR155HGCYFIP1 UBE2Q2P3 MTA2 DDOST MTRNR2L1 DHRS3 MTRNR2L3 EGFL7 PIK3IP1 EIF1AYRPL26 ETS2 RPL27 FCGR2A RPL27A FOLR2 RPL35A GATM RPS11 GBP3 RPS16 GNG2RPS20 GSTT1 RPS26 GYPC SPOCK2 HIST1H1E SYTL3 HPGDS TOB1 IFI44L TPT1IGFBP4 TTN ITGA4 TXNIP KCTD12 WNK1 LGMN ZFP36L2 LOC441081 LTC4S LYVE1MERTK METTL7B MS4A4A MS4A7 MTSS1 NLRP3 OLFML3 PLA2G15 PLXDC2 PMP22 PORPRDX2 PTGS1 RNASE1 ROCK1 RPS4Y1 S100A9 SCAMP2 SEPP1 SESN1 SLC18B1SLC39A1 SLC40A1 SLC7A8 SORL1 SPP1 STAB1 TMEM106C TMEM86A TMEM9 TNFRSF1BTNFRSF21 TPD52L2 ULK3 ZFP36L2

TABLE S6 Table S6. The oncogenic resistance signatures: oncogenic-ICR,exclusion, uICR, and the refined uICR. Genes up-regulated in ICRmalignant cells (1 denotes the gene is Genes down-regulated in ICRmalignant cells (1 denotes the gene is included in the signature, and 0otherwise) included in the signature, and 0 otherwise) uICR-up genesoncogenic-ICR-up Exclusion- uICR-up uICR-down oncogenic- Exclusion-uICR-down (immune resistance) (post treatment) up (refined) genesICR-down down (refined) ACAT1 0 1 0 A2M 1 1 0 ACP5 0 1 0 ACSL3 1 0 0ACTB 1 0 0 ACSL4 1 0 0 ACTG1 0 1 0 ADM 1 0 0 ADSL 0 1 0 AEBP1 1 0 1 AEN1 0 0 AGA 1 1 0 AK2 0 1 0 AHNAK 1 1 1 ANP32E 1 0 0 ANGPTL4 1 0 0 APP 0 10 ANXA1 1 1 0 ASAP1 0 1 0 ANXA2 1 0 0 ATP5A1 1 0 0 APLP2 1 1 0 ATP5D 0 10 APOC2 0 1 1 ATP5G2 1 0 0 APOD 1 0 1 BANCR 0 1 0 APOE 1 0 1 BCAN 0 1 0ARF5 0 1 0 BZW2 1 1 0 ARL6IP5 1 0 0 C17orf76-AS1 1 1 0 ATF3 1 0 0 C1QBP1 1 1 ATP1A1 1 1 0 C20orf112 1 0 0 ATP1B1 1 1 0 C6orf48 0 1 0 ATP1B3 1 00 CA14 1 1 0 ATRAID 0 1 0 CBX5 1 0 0 B2M 1 1 1 CCT2 1 0 1 BACE2 1 0 0CCT3 1 1 0 BBX 1 0 0 CCT6A 0 1 1 BCL6 1 0 0 CDK4 1 0 0 C10orf54 0 1 1CEP170 0 1 0 C4A 0 1 0 CFL1 1 0 0 CALU 1 0 0 CHP1 0 1 0 CASP1 1 0 0CNRIP1 1 0 0 CAST 1 0 0 CRABP2 1 0 0 CAV1 1 0 0 CS 1 0 0 CBLB 0 1 0CTPS1 1 1 0 CCND3 1 1 0 CYC1 0 1 0 CD151 1 1 0 DAP3 0 1 0 CD44 1 0 0DCAF13 1 0 1 CD47 1 1 0 DCT 1 1 0 CD58 1 1 0 DDX21 0 1 0 CD59 1 1 0DDX39B 1 0 0 CD63 1 0 1 DLL3 1 0 0 CD9 1 0 0 EDNRB 0 1 0 CDH19 1 1 0EEF1D 0 1 0 CHI3L1 1 0 0 EEF1G 1 1 0 CHN1 0 1 0 EEF2 0 1 0 CLIC4 1 0 0EIF1AX 0 1 0 CLU 0 1 0 EIF2S3 1 1 0 CPVL 0 1 0 EIF3E 0 1 0 CRELD1 1 0 0EIF3K 1 1 0 CRYAB 1 0 0 EIF3L 0 1 0 CSGALNACT1 1 0 0 EIF4A1 1 1 1 CSPG41 0 0 EIF4EBP2 1 0 0 CST3 1 1 0 ESRP1 0 1 0 CTSA 1 0 0 FAM174B 1 0 0CTSB 1 1 0 FAM178B 0 1 0 CTSD 1 1 1 FAM92A1 0 1 0 CTSL1 1 1 0 FBL 1 0 0DAG1 1 0 0 FBLN1 1 0 0 DCBLD2 1 0 0 FOXRED2 1 0 0 DDR1 1 1 0 FTL 1 1 0DDX5 1 0 0 FUS 1 0 0 DPYSL2 1 1 0 GABARAP 1 0 0 DSCR8 0 1 0 GAS5 1 1 0DUSP4 1 0 0 GNB2L1 1 1 0 DUSP6 1 1 0 GPATCH4 1 0 0 DYNLRB1 0 1 0 GPI 1 10 ECM1 1 0 0 GRWD1 1 0 0 EEA1 1 0 1 GSTO1 0 1 0 EGR1 1 0 0 H3F3A 1 0 0EMP1 1 1 1 H3F3AP4 1 0 0 EPHX2 1 0 0 HMGA1 1 0 0 ERBB3 1 0 0 HNRNPA1 1 00 EVA1A 1 0 0 HNRNPA1P10 1 0 0 EZH1 1 0 0 HNRNPC 1 0 0 EZR 0 1 0 HSPA8 10 0 FAM3C 1 1 0 IDH2 1 0 0 FBXO32 1 0 1 IFI16 0 1 0 FCGR2C 1 0 0 ILF2 11 1 FCRLA 1 0 0 IMPDH2 0 1 0 FGFR1 1 1 0 ISYNA1 1 0 0 FLJ43663 1 0 0ITM2C 1 0 0 FOS 1 0 0 KIAA0101 1 0 0 FYB 0 1 1 LHFPL3-AS1 0 1 0 GAA 1 10 LOC100190986 0 1 0 GADD45B 1 0 0 LYPLA1 0 1 0 GATSL3 0 1 1 MAGEA4 1 01 GEM 1 0 0 MARCKS 0 1 0 GOLGB1 1 0 0 MDH2 1 1 0 GPNMB 1 0 0 METAP2 1 00 GRN 1 1 0 MID1 1 0 0 GSN 1 1 0 MIR4461 1 0 0 HCP5 0 1 1 MLLT11 1 0 0HLA-A 1 0 1 MPZL1 1 0 0 HLA-B 1 1 1 MRPL37 0 1 0 HLA-C 1 1 1 MRPS12 0 10 HLA-E 1 0 1 MRPS21 1 0 0 HLA-F 1 1 1 MYC 0 1 0 HLA-H 1 1 1 NACA 1 0 0HPCAL1 1 0 0 NCL 1 1 0 HSPA1A 1 1 0 NDUFS2 1 0 0 HSPA1B 0 1 0 NF2 0 1 0HTATIP2 1 0 0 NID1 0 1 0 ID2 0 1 0 NOLC1 1 1 0 IFI27L2 0 1 0 NONO 1 0 1IFI35 1 0 0 NPM1 0 1 0 IGF1R 1 0 0 NUCKS1 0 1 0 IL1RAP 1 0 0 OAT 0 1 0IL6ST 1 0 0 PA2G4 1 0 1 ISCU 0 1 0 PABPC1 1 1 0 ITGA3 1 1 1 PAFAH1B3 1 00 ITGA6 1 0 0 PAICS 0 1 0 ITGA7 0 1 0 PFDN2 1 0 0 ITGB1 1 0 0 PFN1 1 0 0ITGB3 1 1 0 PGAM1 1 0 1 ITM2B 1 0 0 PIH1D1 1 0 0 JUN 1 0 0 PLTP 0 1 0KCNN4 1 1 0 PPA1 1 0 1 KLF4 1 0 0 PPIA 1 0 1 KLF6 1 0 0 PPP2R1A 1 0 0KRT10 0 1 0 PSAT1 0 1 0 LAMP2 1 0 1 PSMD4 1 0 0 LEPROT 1 0 0 PTMA 1 0 0LGALS1 1 0 0 PYCARD 0 1 0 LGALS3 1 0 0 RAN 1 0 0 LGALS3BP 1 0 0 RASA3 01 0 LOC100506190 0 1 0 RBM34 1 0 0 LPL 1 0 0 RNF2 1 0 0 LRPAP1 1 0 0RPAIN 1 0 0 LTBP3 0 1 0 RPL10 0 1 0 LYRM9 0 1 1 RPL10A 1 1 0 MAEL 0 1 0RPL11 1 1 0 MAGEC2 1 0 0 RPL12 1 1 0 MAP1B 0 1 0 RPL13 1 1 0 MATN2 0 1 0RPL13A 1 1 0 MFGE8 1 1 1 RPL13AP5 1 1 0 MFI2 1 1 0 RPL14 0 1 0 MIA 1 1 1RPL17 1 1 0 MRPS6 0 1 0 RPL18 1 1 0 MT1E 1 0 0 RPL18A 1 1 1 MT1M 1 0 0RPL21 1 0 0 MT1X 1 0 0 RPL26 1 0 1 MT2A 1 1 0 RPL28 1 1 0 NDRG1 0 1 0RPL29 1 1 0 NEAT1 1 0 0 RPL3 1 1 0 NFKBIA 1 1 0 RPL30 0 1 0 NFKBIZ 1 0 0RPL31 1 0 1 NNMT 1 0 0 RPL35 0 1 0 NPC1 1 1 0 RPL36A 1 0 0 NPC2 1 0 1RPL37 1 0 0 NR4A1 1 0 0 RPL37A 1 1 0 NSG1 1 0 1 RPL39 1 1 0 OCIAD2 0 1 0RPL4 1 1 0 PAGE5 0 1 0 RPL41 1 0 0 PDK4 1 0 0 RPL5 1 1 0 PERP 0 1 0 RPL61 1 0 PKM 0 1 0 RPL7 0 1 0 PLP2 1 0 0 RPL7A 0 1 0 PRKCDBP 1 0 0 RPL8 1 10 PRNP 1 0 0 RPLP0 1 1 0 PROS1 1 0 1 RPLP1 1 1 0 PRSS23 1 0 0 RPS10 1 10 PSAP 1 0 0 RPS11 1 1 1 PSMB9 1 0 0 RPS12 1 0 0 PTRF 1 0 0 RPS15 0 1 1RDH5 0 1 1 RPS15A 1 1 0 RNF145 1 0 0 RPS16 1 1 0 RPS4Y1 1 0 0 RPS17 1 10 S100A13 0 1 0 RPS17L 1 1 0 S100A6 1 1 0 RPS18 1 1 0 S100B 1 0 0 RPS191 1 0 SAT1 1 0 0 RPS2 0 1 0 SCARB2 1 0 0 RPS21 1 0 1 SCCPDH 1 0 0 RPS231 0 0 SDC3 1 0 0 RPS24 1 1 0 SEL1L 1 0 0 RPS26 1 0 0 SEMA3B 1 0 0 RPS271 1 0 SERPINA1 0 1 1 RPS27A 1 0 0 SERPINA3 1 1 0 RPS3 1 1 0 SERPINE2 1 10 RPS3A 0 1 0 SGCE 1 1 0 RPS4X 1 1 0 SGK1 1 0 0 RPS5 1 1 1 SLC20A1 1 0 0RPS6 1 0 0 SLC26A2 1 1 0 RPS7 1 1 0 SLC39A14 1 0 0 RPS8 1 1 0 SLC5A3 1 10 RPS9 1 1 0 SNX9 0 1 0 RPSA 1 1 0 SOD1 1 0 0 RSL1D1 0 1 0 SPON2 0 1 0RUVBL2 1 0 1 SPRY2 1 0 0 SAE1 1 0 1 SQSTM1 1 0 0 SCD 1 1 0 SRPX 1 0 0SCNM1 1 0 0 STOM 1 0 0 SERBP1 0 1 0 SYNGR2 1 0 0 SERPINF1 1 1 0 SYPL1 10 0 SET 1 0 0 TAPBP 1 0 1 SF3B4 1 0 0 TAPBPL 1 0 0 SHMT2 1 0 0 TF 1 0 0SKP2 1 0 0 TGOLN2 1 0 0 SLC19A1 0 1 0 THBD 0 1 0 SLC25A3 1 0 0 TIMP1 1 10 SLC25A5 0 1 0 TIMP2 1 0 1 SLC25A6 0 1 0 TIMP3 1 0 0 SMS 1 0 0 TIPARP 10 0 SNAI2 1 1 0 TM4SF1 1 1 0 SNHG16 0 1 0 TMBIM6 0 1 0 SNHG6 1 1 0TMED10 1 0 0 SNRPE 1 0 1 TMED9 1 0 0 SORD 0 1 0 TMEM66 1 0 0 SOX4 1 1 0TMX4 1 0 0 SRP14 1 0 0 TNC 1 0 0 SSR2 1 0 0 TNFSF4 0 1 1 TIMM13 0 1 0TPP1 1 1 0 TIMM50 1 1 0 TRIML2 0 1 1 TMC6 1 0 0 TSC22D3 1 1 0 TOP1MT 0 10 TSPYL2 0 1 0 TP53 1 0 0 TXNIP 0 1 0 TRAP1 0 1 0 TYR 1 0 0 TRPM1 1 0 0UBC 1 1 0 TSR1 1 0 0 UPP1 1 0 0 TUBA1B 1 0 0 XAGE1A 0 1 0 TUBB 1 0 0XAGE1B 0 1 0 TUBB4A 0 1 0 XAGE1C 0 1 0 TULP4 1 0 0 XAGE1D 0 1 0 TXLNA 01 0 XAGE1E 0 1 0 TYRP1 0 1 0 ZBTB20 1 0 0 UBA52 1 0 1 ZBTB38 1 0 0 UCK20 1 0 UQCRFS1 1 1 0 UQCRH 1 0 1 USP22 1 0 0 VCY1B 1 0 0 VDAC2 1 0 1VPS72 1 0 0 YWHAE 1 0 0 ZFAS1 0 1 0 ZNF286A 1 0 0

TABLE S7 Genes differentially expressed in CD8 T cells of the CB patientcompared to those of the ICR patients. Up-regulated in CB vs. ICRDown-regulated in CB vs. ICR ALOX5AP AKIRIN2 C1D APIP C3orf14 ARL5A CCL5ASF1B CCR2 ATP6V0C CD52 ATP9B CDC26 BRAT1 CIDECP BRD7 CISH C17orf89COX5B C1GALT1C1 CRIP1 C4orf48 CTSW CALR CXCR6 CCDC137 DDX3Y CDC73 EDF1CDCA7 EIF1AY CDK1 FAM127B CENPM FASLG CEP78 FAU CHMP6 FCGR3A CITED2 FTLCLINT1 GZMA CMTM7 GZMB COTL1 GZMH CRIPT HCG26 CSNK1G3 HCST CYB5R4 HLA-ADCPS HLA-C DNAJB14 HLA-DQA2 DND1 HLA-H DPH3 HSPA1B EFR3A ID2 EMC2 KDM5DEML3 LAIR2 FAM160B1 MIR4461 FAM168B MTRNR2L1 FAM46C MTRNR2L10 FAM53CMTRNR2L6 FAM69A NACA FARSB NCF4 FBXO22 NDUFA13 FEM1A NDUFS5 FTSJD1NDUFV2 GATAD2A RBPJ GET4 RNASEK GGA3 RPL10 GLTSCR2 RPL11 GNL3 RPL12GOLT1B RPL13 GPR137B RPL13AP5 GTDC1 RPL15 HIST1H1E RPL17 HMGA1 RPL18HMHA1 RPL18A HSF1 RPL19 IARS2 RPL21 IL6ST RPL23 JUNB RPL23A KATNA1 RPL24KIAA1429 RPL26 LATS1 RPL29 LOC100294145 RPL30 LRIG2 RPL32 MAN2A1 RPL35MAP3K2 RPL35A MB21D1 RPL36 MCM2 RPL36AL MCM4 RPL37A MED23 RPL4 MGEA5RPL41 MPLKIP RPL6 MRPS33 RPL7 MZT1 RPL7A NAGK RPL9 NEK1 RPLP1 NOA1 RPLP2NPC2 RPS10 NUDT1 RPS11 NUP107 RPS12 OSGEP RPS13 PARP10 RPS14 PELI1 RPS15PGS1 RPS15A PITHD1 RPS16 PLEKHF2 RPS18 POLR3E RPS19 PPIF RPS20 PPP1R21RPS24 PRKAB1 RPS25 PSMD2 RPS27 PTGDR RPS27A PYGO2 RPS3 RAB11B RPS3ARABEP1 RPS4X RALB RPS4Y1 REC8 RPS5 REEP4 RPS6 RNF216P1 RPS8 RNF219 SAMD3RPIA SELM RPS6KA5 SH3BGRL3 RPSAP58 SYMPK SFSWAP TMSB10 SGSM2 TMSB4XSLC1A5 TNFSF4 SLC25A26 TPT1 SLC33A1 TXLNG2P SLC39A3 SLC7A5 SMC1A SMC4SNX4 SPPL2A STAT1 STX17 SYPL1 TAF1B TAF6 TCERG1 TCF7 TEKT4P2 TERF2IPTIMM44 TMEM161B TMEM170A TMEM189 TMEM69 TMX4 TNIP1 TNPO1 TOP2A TPX2TRIB2 TSC22D1 TUBGCP3 TYMS UBA5 UBE2J1 UBE2Q2 UBE2T USP38 UVRAG WDR18ZBED6 ZBTB20 ZFYVE28 ZNF259 ZNF511

TABLE S8 Table S8. Cell-cycle signatures specific to CD8 T cells.Up-regulated in Down-regulated in cycling CD8 T cells cycling CD8 Tcells ACTG1 AOAH ANXA5 ATHL1 ARHGDIB C11orf21 ARL6IP1 CCL3L1 ARPC2 CD37ATP5L CISH CD74 CX3CR1 CNTRL DENND2D CORO1A GNPDA1 COTL1 GZMM COX6A1IL11RA COX6C IL7R COX8A KLRB1 DDOST LDLRAP1 GALM LINC00612 GMFG LY9 GNG5NR4A3 HLA-DRA PDGFD HP1BP3 PLCB2 LCP1 PTGDR LRRFIP1 RAB37 MPC2 RPS27MT2A SORL1 NDUFA4 TRIM22 NDUFC2 TRMU NUP50 TTN PCBP1 UPRT PKM ZNF121POLR2A PSMB2 SNX1 SRRM1 TMA7 VIM YWHAE YWHAQ

TABLE S9 The topmost differentially expressed gene sets in the malignantcells from ICR vs. TN tumors t-test p-value (−log10(|P|), positive =higher in ICR, negative = lower in ICR) mixed N = No. of t-test effectsgenes in the N · qc = No. of Gene set p-value p-value gene set usedgenes N/N · qc GO_RESPONSE_TO_ENDOPLASMIC_RETICULUM_STRESS −36.49 −4.05233 147 0.63 GO_CELLULAR_COPPER_ION_HOMEOSTASIS −44.3 −4.04 13 9 0.69GO_CELLULAR_RESPONSE_TO_ZINC_ION −215.84 −4 16 7 0.44ENDOPLASMIC_RETICULUM_MEMBRANE −42.56 −3.93 85 55 0.65GO_REGULATION_OF_ENDOTHELIAL_CELL_APOPTOTIC_(—) −52.39 −3.79 42 14 0.33PROCESS METALLOTHIONEINS −208.11 −3.72 13 6 0.46GO_INTRAMOLECULAR_OXIDOREDUCTASE_ACTIVITY_(—) −40.53 −3.64 22 14 0.64TRANSPOSING_S_S_BONDS NUCLEAR_ENVELOPE_ENDOPLASMIC_RETICULUM_(—) −38.41−3.59 94 62 0.66 NETWORK GO_CELLULAR_RESPONSE_TO_VITAMIN_D −78.74 −3.5614 4 0.29 KEGG_SNARE_INTERACTIONS_IN_VESICULAR_TRANSPORT −17.6 −3.43 3823 0.61 ENDOPLASMIC_RETICULUM_PART −44.43 −3.43 97 65 0.67GO_COPPER_ION_HOMEOSTASIS −38.11 −3.38 16 12 0.75KEGG_ECM_RECEPTOR_INTERACTION −163.89 −3.35 84 35 0.42GO_ENDOPLASMIC_RETICULUM_GOLGI_INTERMEDIATE_(—) −40.27 −3.3 105 64 0.61COMPARTMENT GO_BLOOD_VESSEL_MORPHOGENESIS −153.28 −3.3 364 117 0.32GO_PLATELET_DERIVED_GROWTH_FACTOR_RECEPTOR_(—) −62.32 −3.24 15 5 0.33BINDING GO_ANGIOGENESIS −148.37 −3.23 293 102 0.35GO_RESPONSE_TO_ZINC_ION −76.24 −3.22 55 21 0.38 PID_INTEGRIN_CS_PATHWAY−172.58 −3.19 26 9 0.35 GOLGI_MEMBRANE −53.05 −3.13 45 26 0.58GO_TRANSITION_METAL_ION_TRANSMEMBRANE_(—) −61.25 −3.12 39 19 0.49TRANSPORTER_ACTIVITY POSITIVE_REGULATION_OF_CELL_PROLIFERATION −31.46−3.11 149 48 0.32 GO_MUSCLE_CELL_MIGRATION −164.41 −3.11 18 10 0.56NUCLEAR ORPHAN RECEPTOR −83.44 −3.09 3 2 0.67GO_POSITIVE_REGULATION_OF_EXTRINSIC_APOPTOTIC_(—) −75.37 −3.08 17 110.65 SIGNALING_PATHWAY_VIA_DEATH_DOMAIN_RECEPTORSGO_PHOSPHOTRANSFERASE_ACTIVITY_FOR_OTHER_(—) −32.33 −3.07 19 11 0.58SUBSTITUTED_PHOSPHATE_GROUPS ST_INTERLEUKIN_13_PATHWAY −2.38 −3.03 7 20.29 WOUND_HEALING −148 −3.02 54 13 0.24 C/EBP −38.85 −3 10 3 0.3GO_INSULIN_LIKE_GROWTH_FACTOR_BINDING −62.71 −2.98 25 11 0.44MUSCLE_DEVELOPMENT −122.53 −2.98 93 29 0.31GO_PLATELET_ALPHA_GRANULE_MEMBRANE −104.99 −2.96 13 7 0.54GO_MANNOSIDASE_ACTIVITY −28.46 −2.95 15 5 0.33GO_POSITIVE_REGULATION_OF_ADHERENS_JUNCTION_(—) −61.36 −2.95 21 9 0.43ORGANIZATION GO_NEGATIVE_REGULATION_OF_EPITHELIAL_CELL_(—) −70.48 −2.9535 8 0.23 APOPTOTIC_PROCESS ENDOPLASMIC_RETICULUM −50.01 −2.94 294 1800.61 CELL_FATE_COMMITMENT −72.59 −2.94 13 3 0.23GO_ENDOPLASMIC_RETICULUM_GOLGI_INTERMEDIATE_(—) −65.43 −2.93 63 38 0.6COMPARTMENT_MEMBRANE GO_NEGATIVE_REGULATION_OF_INTERLEUKIN_8_(—) −126.57−2.93 15 5 0.33 PRODUCTION PID_TNF_PATHWAY −73 −2.92 46 22 0.48GO_RECEPTOR_REGULATOR_ACTIVITY −92.97 −2.92 45 10 0.22GO_EXTRACELLULAR_STRUCTURE_ORGANIZATION −107.25 −2.92 304 111 0.37ER_GOLGI_INTERMEDIATE_COMPARTMENT −12.41 −2.91 24 20 0.83GO_RESPONSE_TO_CADMIUM_ION −124.5 −2.9 40 25 0.62GO_HEPARAN_SULFATE_PROTEOGLYCAN_(—) −31.95 −2.89 23 8 0.35BIOSYNTHETIC_PROCESS GO_AXON_REGENERATION −144.4 −2.88 24 9 0.38ENDOMEMBRANE_SYSTEM −21.95 −2.87 220 137 0.62HALLMARK_IL6_JAK_STAT3_SIGNALING −170.22 −2.87 87 40 0.46GO_HEPARAN_SULFATE_PROTEOGLYCAN_(—) −30.74 −2.86 28 8 0.29METABOLIC_PROCESS GO_POSITIVE_REGULATION_OF_CELL_(—) −88.33 −2.85 24 110.46 JUNCTION_ASSEMBLY GO_VASCULATURE_DEVELOPMENT −143.79 −2.84 469 1530.33 CELLULAR_CATION_HOMEOSTASIS −96.84 −2.83 106 32 0.3GO_CELL_SUBSTRATE_JUNCTION_ASSEMBLY −79.64 −2.82 41 19 0.46PID_FRA_PATHWAY −55.92 −2.81 37 17 0.46GO_REGULATION_OF_ADHERENS_JUNCTION_(—) −63.38 −2.81 50 22 0.44ORGANIZATION GO_CELL_ADHESION_MEDIATED_BY_INTEGRIN −81.75 −2.81 12 80.67 GO_SARCOLEMMA −216.58 −2.81 125 37 0.3GO_NEGATIVE_REGULATION_OF_ENDOTHELIAL_(—) −38.24 −2.8 27 7 0.26CELL_APOPTOTIC_PROCESS GO_CORECEPTOR_ACTIVITY −68.21 −2.79 38 11 0.29GO_REGULATION_OF_INTERLEUKIN_8_(—) −12.85 −2.78 12 3 0.25BIOSYNTHETIC_PROCESS REACTOME_EXTRINSIC_PATHWAY_FOR_APOPTOSIS −55.38−2.78 13 8 0.62 HALLMARK_HYPOXIA −112.24 −2.78 200 116 0.58GO_ER_NUCLEUS_SIGNALING_PATHWAY −28.31 −2.75 34 25 0.74HOMOPHILIC_CELL_ADHESION −55 −2.74 16 4 0.25 GO_SNAP_RECEPTOR_ACTIVITY−20.16 −2.73 38 22 0.58 HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION−128.55 −2.73 200 110 0.55 GO_CELLULAR_RESPONSE_TO_CADMIUM_ION −155.05−2.73 15 9 0.6 GO_BASAL_LAMINA −27.9 −2.72 21 6 0.29 CELL_CELL_ADHESION−40.27 −2.72 86 19 0.22 POSITIVE_REGULATION_OF_MULTICELLULAR_(—) −45.51−2.71 66 18 0.27 ORGANISMAL_PROCESS FIBROBLAST −73.88 −2.71 6 3 0.5GO_ATPASE_COMPLEX −80.46 −2.7 24 7 0.29GO_INTRINSIC_COMPONENT_OF_EXTERNAL_(—) −68.1 −2.69 27 7 0.26SIDE_OF_PLASMA_MEMBRANE PID_INTEGRIN3_PATHWAY −78.14 −2.68 43 22 0.51CATION_HOMEOSTASIS −93.05 −2.68 109 32 0.29 GO_CELL_SUBSTRATE_ADHESION−162.02 −2.68 164 58 0.35 GO_INTRINSIC_APOPTOTIC_SIGNALING_(—) −37.62−2.67 32 18 0.56 PATHWAY_IN_RESPONSE_TO_(—) ENDOPLASMIC_RETICULUM_STRESSGO_POSITIVE_REGULATION_OF_CELL_MATRIX_(—) −57.1 −2.66 40 15 0.38ADHESION GO_NEGATIVE_REGULATION_OF_GLYCOPROTEIN_(—) −60.19 −2.66 15 100.67 METABOLIC_PROCESS GO_NEGATIVE_REGULATION_OF_TYPE_2_(—) −162.47−2.66 11 4 0.36 IMMUNE_RESPONSE REACTOME_ACTIVATION_OF_CHAPERONES_(—)−22.85 −2.64 13 8 0.62 BY_ATF6_ALPHA GO_NEGATIVE_REGULATION_OF_DNA_(—)−13.63 −2.63 16 8 0.5 RECOMBINATION GO_CELLULAR_RESPONSE_TO_(—) −22.73−2.63 122 81 0.66 TOPOLOGICALLY_INCORRECT_PROTEINGO_CELLULAR_RESPONSE_TO_CALCIUM_ION −69.45 −2.63 49 18 0.37GO_SECRETORY_GRANULE_MEMBRANE −133.42 −2.63 78 28 0.36GOLGI_VESICLE_TRANSPORT −13.68 −2.62 48 37 0.77REACTOME_DIABETES_PATHWAYS −20.26 −2.62 133 80 0.6GO_NEGATIVE_REGULATION_OF_GLYCOPROTEIN_(—) −23.98 −2.61 12 9 0.75BIOSYNTHETIC_PROCESS CAHOY_ASTROGLIAL −197.11 −2.61 100 37 0.37GO_HEMIDESMOSOME_ASSEMBLY −95.2 −2.6 12 5 0.42 GO_FIBRINOLYSIS −98.47−2.6 21 6 0.29 GO_PROTEIN_COMPLEX_INVOLVED_IN_CELL_(—) −171.74 −2.6 3010 0.33 ADHESION ST_IL_13_PATHWAY −1.56 −2.59 7 2 0.29POSITIVE_REGULATION_OF_PROTEIN_(—) −37.38 −2.58 29 9 0.31MODIFICATION_PROCESS HALLMARK_UV_RESPONSE_UP −67.14 −2.57 158 93 0.59CELL_MIGRATION −87.02 −2.57 96 34 0.35 ATPASE_ACTIVITY_COUPLED_TO_(—)−130.2 −2.57 20 5 0.25 TRANSMEMBRANE_MOVEMENT_OF_(—)IONS_PHOSPHORYLATIVE_MECHANISM GO_INTEGRIN_BINDING −94.95 −2.56 105 480.46 HALLMARK_TNFA_SIGNALING_VIA_NFKB −154.11 −2.56 200 108 0.54GO_PLATELET_ALPHA_GRANULE −164.05 −2.56 75 35 0.47 PID_INTEGRIN1_PATHWAY−89.65 −2.55 66 34 0.52 GO_CATION_TRANSPORTING_ATPASE_COMPLEX −119.68−2.55 16 4 0.25 PROTEIN_AMINO_ACID_LIPIDATION −35.59 −2.54 24 19 0.79GO_NEGATIVE_REGULATION_OF_LIPID_STORAGE −92.01 −2.54 17 6 0.35GO_BASEMENT_MEMBRANE_ORGANIZATION −26.24 −2.53 11 7 0.64POSITIVE_REGULATION_OF_CYTOKINE_PRODUCTION −41.65 −2.53 15 5 0.33BIOCARTA_SODD_PATHWAY −37.42 −2.52 10 8 0.8GO_PERK_MEDIATED_UNFOLDED_PROTEIN_RESPONSE −39.21 −2.52 12 10 0.83PHOSPHOLIPID_METABOLIC_PROCESS −44.83 −2.52 74 42 0.57 Targets ofNFAT_Q6 −53.2 −2.52 246 80 0.33 BIOCARTA_STRESS_PATHWAY −71.76 −2.52 2510 0.4 CYTOPLASM_ORGANIZATION_AND_BIOGENESIS −67.96 −2.51 15 7 0.47Targets of FREAC3_01 −23.28 −2.5 251 65 0.26 GO_COLLAGEN_BINDING −84.49−2.5 65 27 0.42 PID_INTEGRIN4_PATHWAY −35.75 −2.49 11 4 0.36CELL_SURFACE −42.55 −2.49 79 27 0.34GO_PHOSPHATE_TRANSMEMBRANE_TRANSPORTER_(—) −14.32 −2.48 30 16 0.53ACTIVITY NAIVE_VS_ACTIVATED_CD8_TCELL_DN −38.5 −2.48 200 81 0.4MEMBRANE_LIPID_BIOSYNTHETIC_PROCESS −20.36 −2.47 49 29 0.59GO_GLYCEROPHOSPHOLIPID_CATABOLIC_PROCESS −27.95 −2.47 13 7 0.54GO_INTERSTITIAL_MATRIX −81.96 −2.47 14 3 0.21GO_REGULATION_OF_EXTRINSIC_(—) −103.8 −2.47 55 32 0.58APOPTOTIC_SIGNALING_PATHWAY_(—) VIA_DEATH_DOMAIN_RECEPTORSINORGANIC_ANION_TRANSPORT −151.85 −2.47 18 4 0.22REACTOME_CLASS_B_2_SECRETIN_FAMILY_(—) −62.64 −2.46 88 19 0.22 RECEPTORSGO_DECIDUALIZATION −99.39 −2.46 21 7 0.33GO_MULTI_MULTICELLULAR_ORGANISM_PROCESS −129.03 −2.46 213 62 0.29NABA_BASEMENT_MEMBRANES −19.18 −2.45 40 12 0.3GO_PROTEINACEOUS_EXTRACELLULAR_MATRIX −60.88 −2.45 356 86 0.24GO_EXTRACELLULAR_MATRIX −121.18 −2.45 426 116 0.27GO_INTEGRIN_MEDIATED_SIGNALING_PATHWAY −129.11 −2.45 82 36 0.44SECRETION −34.16 −2.44 178 68 0.38GO_CARBOHYDRATE_DERIVATIVE_CATABOLIC_(—) −62.72 −2.44 174 76 0.44PROCESS HALLMARK_APOPTOSIS −184.35 −2.44 161 111 0.69LIPOPROTEIN_METABOLIC_PROCESS −34.84 −2.43 33 21 0.64LIPOPROTEIN_BIOSYNTHETIC_PROCESS −36.48 −2.43 26 19 0.73GO_BASEMENT_MEMBRANE −54.56 −2.43 93 32 0.34REACTOME_UNFOLDED_PROTEIN_RESPONSE −13.89 −2.42 80 58 0.72GO_LIPOPROTEIN_BIOSYNTHETIC_PROCESS −63.03 −2.42 85 40 0.47GO_HYDROLASE_ACTIVITY_ACTING_ON_(—) −65.4 −2.42 122 44 0.36GLYCOSYL_BONDS GO_REGULATION_OF_VIRAL_ENTRY_INTO_(—) −72.07 −2.42 28 120.43 HOST_CELL BIOCARTA_IL1R_PATHWAY −72.17 −2.41 33 12 0.36HALLMARK_IL2_STAT5_SIGNALING −199.12 −2.41 200 91 0.46GO_NEGATIVE_REGULATION_OF_SMALL_(—) −72.25 −2.4 40 14 0.35GTPASE_MEDIATED_SIGNAL_TRANSDUCTION GO_GROWTH_FACTOR_BINDING −107.54−2.39 123 46 0.37 GO_METALLOENDOPEPTIDASE_INHIBITOR_(—) −118.81 −2.39 145 0.36 ACTIVITY TTAYRTAA_Targets of E4BP4_01 −133.15 −2.39 265 74 0.28GO_REGULATION_OF_T_HELPER_2_CELL_(—) −200.19 −2.39 11 3 0.27DIFFERENTIATION CELL_ACTIVATION −24.51 −2.38 77 17 0.22GO_EXTRACELLULAR_MATRIX_COMPONENT −46.21 −2.38 125 47 0.38GO_RESPONSE_TO_AXON_INJURY −138.03 −2.38 48 19 0.4GO_FORMATION_OF_PRIMARY_GERM_LAYER −93.37 −2.37 110 33 0.3HYDROLASE_ACTIVITY_ACTING_ON_ACID_(—) −126.69 −2.37 39 14 0.36ANHYDRIDESCATALYZING_TRANSMEMBRANE_(—) MOVEMENT_OF_SUBSTANCESGO_CELLULAR_RESPONSE_TO_(—) −41.78 −2.36 24 10 0.42PROSTAGLANDIN_STIMULUS GO_NEGATIVE_REGULATION_OF_(—) −55.1 −2.36 12 60.5 MULTICELLULAR_ORGANISMAL_(—) METABOLIC_PROCESSGO_NEGATIVE_REGULATION_OF_GROWTH −96.42 −2.36 236 85 0.36GO_REGULATION_OF_ERK1_AND_ERK2_CASCADE −121.13 −2.36 238 74 0.31GO_CELL_MATRIX_ADHESION −156.53 −2.36 119 42 0.35 PID_P38_MKK3_6PATHWAY−11.76 −2.35 26 9 0.35 GO_ACROSOMAL_MEMBRANE −98.54 −2.35 22 8 0.36BLOOD_COAGULATION −133.89 −2.35 43 12 0.28GO_REGULATION_OF_INTERLEUKIN_2_PRODUCTION −134.5 −2.35 48 19 0.4GO_IRE1_MEDIATED_UNFOLDED_PROTEIN_RESPONSE −17.39 −2.34 56 44 0.79GO_PROTEIN_HETEROOLIGOMERIZATION −31.48 −2.34 113 44 0.39GO_NEGATIVE_REGULATION_OF_SODIUM_ION_(—) −110.08 −2.34 11 4 0.36TRANSPORT MEMBRANE_FUSION −27.59 −2.33 28 15 0.54KEGG_GLYCOSPHINGOLIPID_BIOSYNTHESIS_(—) −46.27 −2.33 15 6 0.4GANGLIO_SERIES GO_REGULATION_OF_CELL_SUBSTRATE_ADHESION −62.07 −2.33 17367 0.39 GO_REGULATION_OF_PROTEIN_GLYCOSYLATION −18.5 −2.32 14 5 0.36GO_PLASMA_MEMBRANE_FUSION −40.77 −2.32 26 8 0.31 GO_COMPACT_MYELIN−55.03 −2.31 15 8 0.53 Targets of CDPCR1_01 −74.82 −2.31 130 33 0.25AMINO_ACID_DERIVATIVE_BIOSYNTHETIC_PROCESS −10.41 −2.3 10 4 0.4KEGG_GLYCOSAMINOGLYCAN_BIOSYNTHESIS_(—) −61.3 −2.3 22 6 0.27CHONDROITIN_SULFATE GO_REGULATION_OF_CELL_MATRIX_ADHESION −63.63 −2.3 9034 0.38 GO_ANTIMICROBIAL_HUMORAL_RESPONSE −81.25 −2.3 52 14 0.27GO_NEGATIVE_REGULATION_OF_PROTEIN_(—) −47.66 −2.29 36 16 0.44KINASE_B_SIGNALING GO_RESPONSE_TO_OXYGEN_LEVELS −69.16 −2.29 311 1270.41 GO_RESPONSE_TO_TRANSITION_METAL_(—) −89.78 −2.29 148 63 0.43NANOPARTICLE GO_FIBRONECTIN_BINDING −106.39 −2.29 28 16 0.57GO_POSITIVE_REGULATION_OF_INTERLEUKIN_(—) −147.35 −2.29 31 12 0.392_PRODUCTION GO_ENDOPLASMIC_RETICULUM_LUMEN −32.2 −2.28 201 84 0.42GO_POSITIVE_REGULATION_OF_EXTRINSIC_(—) −52.86 −2.28 53 35 0.66APOPTOTIC_SIGNALING_PATHWAY GO_CELLULAR_RESPONSE_TO_OXYGEN_LEVELS −58.67−2.28 143 55 0.38 REACTOME_INTEGRIN_CELL_SURFACE_(—) −89.69 −2.28 79 370.47 INTERACTIONS EXTRACELLULAR_REGION_PART −125.68 −2.28 338 88 0.26GO_SECRETORY_GRANULE_LUMEN −157.29 −2.28 85 31 0.36 GO_SNARE_COMPLEX−17.36 −2.27 53 28 0.53 KEGG_GLYCOSAMINOGLYCAN_DEGRADATION −47 −2.27 219 0.43 ATPASE_ACTIVITY_COUPLED_TO_(—) −133.35 −2.27 24 9 0.38TRANSMEMBRANE_MOVEMENT_OF_IONS GO_NEGATIVE_REGULATION_OF_COAGULATION−196.17 −2.27 48 13 0.27 REACTOME_TRANSPORT_OF_VITAMINS_(—) −10.14 −2.2631 9 0.29 NUCLEOSIDES_AND_RELATED_MOLECULES GO_IRON_ION_BINDING −18.16−2.26 163 42 0.26 GO_ACETYLGLUCOSAMINYLTRANSFERASE_(—) −38.97 −2.26 4919 0.39 ACTIVITY GO_POSITIVE_REGULATION_OF_RECEPTOR_(—) −75.46 −2.26 4713 0.28 MEDIATED_ENDOCYTOSIS HALLMARK_UV_RESPONSE_DN −95.37 −2.26 144 640.44 GO_CELL_ADHESION_MOLECULE_BINDING −113.26 −2.26 186 74 0.4REACTOME_CELL_SURFACE_INTERACTIONS_(—) −148.37 −2.26 91 38 0.42AT_THE_VASCULAR_WALL GO_UBIQUITIN_UBIQUITIN_LIGASE_ACTIVITY −10.5 −2.2513 7 0.54 GO_N_GLYCAN_PROCESSING −37.67 −2.25 20 5 0.25GO_BRANCH_ELONGATION_OF_AN_EPITHELIUM −38.53 −2.25 17 4 0.24REACTOME_TRANSPORT_OF_GLUCOSE_AND_(—) −70.87 −2.25 89 18 0.2OTHER_SUGARS_BILE_SALTS_AND_ORGANIC_(—)ACIDS_METAL_IONS_AND_AMINE_COMPOUNDS GO_BASAL_PLASMA_MEMBRANE −102.25−2.25 33 9 0.27 GO_PLATELET_DEGRANULATION −156.77 −2.25 107 51 0.48PDZ_DOMAIN_BINDING −29.47 −2.24 14 4 0.29 BIOCARTA_GATA3_PATHWAY −52.32−2.24 16 4 0.25 GO_NEGATIVE_REGULATION_OF_CELL_(—) −81.77 −2.24 53 250.47 SUBSTRATE_ADHESION AMINE_BIOSYNTHETIC_PROCESS −12.25 −2.23 15 70.47 GO_REGULATION_OF_RECEPTOR_ACTIVITY −13.59 −2.23 117 30 0.26GO_PYRIMIDINE_NUCLEOSIDE_CATABOLIC_PROCESS −79.61 −2.23 21 8 0.38GO_CIRCULATORY_SYSTEM_DEVELOPMENT −132.21 −2.23 788 233 0.3GO_MATURE_B_CELL_DIFFERENTIATION −21.54 −2.22 17 7 0.41GO_OLIGOSACCHARIDE_CATABOLIC_PROCESS −24.14 −2.22 12 7 0.58GO_RESPONSE_TO_PROSTAGLANDIN −38.47 −2.22 34 11 0.32GO_OXIDOREDUCTASE_ACTIVITY_ACTING_ON_(—) −60.87 −2.22 15 5 0.33THE_CH_NH2_GROUP_OF_DONORS_OXYGEN_(—) AS_ACCEPTORST_TUMOR_NECROSIS_FACTOR_PATHWAY −96.67 −2.22 29 17 0.59GO_REGULATION_OF_INTERLEUKIN_8_SECRETION −101.51 −2.22 19 8 0.42GO_REGULATION_OF_MEMBRANE_PROTEIN_(—) −157.88 −2.22 21 9 0.43ECTODOMAIN_PROTEOLYSIS ER_TO_GOLGI_VESICLE_MEDIATED_TRANSPORT −4.83−2.21 18 15 0.83 PID_TCR_JNK_PATHWAY −26.53 −2.21 14 6 0.43REACTOME_IL1_SIGNALING −34.94 −2.21 39 16 0.41GO_POSITIVE_REGULATION_OF_IMMUNOGLOBULIN_(—) −97.18 −2.21 11 4 0.36SECRETION PID_API_PATHWAY −129.76 −2.2 70 31 0.44 Targets of LMO2COM_01−20.1 −2.19 264 72 0.27 GO_RESPONSE_TO_STARVATION −41.8 −2.19 154 700.45 GO_MEMBRANE_RAFT_ORGANIZATION −114.17 −2.19 17 12 0.71 COAGULATION−131.28 −2.19 44 12 0.27 GO_SULFATE_TRANSPORT −73.24 −2.18 14 3 0.21Targets of STAT5A_02 −73.82 −2.18 141 42 0.3 GO_SECRETORY_GRANULE −145.5−2.18 352 114 0.32 GO_REGULATION_OF_COAGULATION −149.09 −2.18 88 26 0.3GO_CELL_SURFACE −169.9 −2.18 757 217 0.29GO_NUCLEOTIDE_TRANSMEMBRANE_TRANSPORT −6.85 −2.17 12 7 0.58PROTEIN_TRANSPORTER_ACTIVITY −7.67 −2.17 14 7 0.5ENDOPLASMIC_RETICULUM_LUMEN −16.08 −2.17 14 12 0.86GO_REGULATION_OF_PEPTIDYL_SERINE_(—) −34.4 −2.17 118 37 0.31PHOSPHORYLATION LIPID_RAFT −83.19 −2.17 29 16 0.55GO_CELLULAR_RESPONSE_TO_EXTERNAL_STIMULUS −74.77 −2.16 264 114 0.43GO_REGULATION_OF_EXTRINSIC_APOPTOTIC_(—) −97.55 −2.16 153 77 0.5SIGNALING_PATHWAY GO_RESPONSE_TO_DRUG −144.63 −2.16 431 159 0.37GO_REGULATION_OF_EXTRACELLULAR_MATRIX_(—) −147.71 −2.16 14 4 0.29DISASSEMBLY REACTOME_ACTIVATION_OF_CHAPERONE_(—) −15.37 −2.15 46 35 0.76GENES_BY_XBP1S GO_DENDRITE_MORPHOGENESIS −17.1 −2.15 42 12 0.29GO_MATURE_B_CELL_DIFFERENTIATION_(—) −27.87 −2.15 13 6 0.46INVOLVED_IN_IMMUNE_RESPONSE GO_CELLULAR_RESPONSE_TO_MECHANICAL_STIMULUS−133.12 −2.15 80 32 0.4 GO_HETEROTYPIC_CELL_CELL_ADHESION −138.66 −2.1527 9 0.33 BIOCARTA_LYM_PATHWAY −58.96 −2.14 11 7 0.64 HINATA_NFKB_MATRIX−78.15 −2.14 10 7 0.7 GO_NEGATIVE_REGULATION_OF_RHO_PROTEIN_(—) −83.78−2.14 14 8 0.57 SIGNAL_TRANSDUCTION GO_TELOMERE_LOCALIZATION −8.52 −2.1311 4 0.36 INTRINSIC_TO_ENDOPLASMIC_RETICULUM_(—) −11.39 −2.13 24 14 0.58MEMBRANE CELLULAR_HOMEOSTASIS −61.96 −2.13 147 45 0.31GO_CELL_MIGRATION_INVOLVED_IN_SPROUTING_(—) −87.08 −2.13 15 4 0.27ANGIOGENESIS GO_GASTRULATION −36.59 −2.12 155 46 0.3 PID_IL1_PATHWAY−68.25 −2.12 34 15 0.44 GO_ENDOPEPTIDASE_ACTIVITY −81.63 −2.12 448 1350.3 INTEGRAL_TO_ENDOPLASMIC_RETICULUM_MEMBRANE −9.58 −2.11 24 14 0.58REACTOME_ACTIVATION_OF_CHAPERONE_GENES_BY_(—) −16.39 −2.11 11 7 0.64ATF6_ALPHA GO_ZINC_II_ION_TRANSPORT −38.12 −2.11 26 13 0.5RYAAAKNNNNNNTTGW_UNKNOWN −51.33 −2.11 84 22 0.26 GGARNTKYCCA_UNKNOWN−56.64 −2.11 78 24 0.31 GO_MEMBRANE_HYPERPOLARIZATION −86.71 −2.11 11 30.27 PID_INTEGRIN_A9B1_PATHWAY −88.11 −2.11 25 11 0.44GO_MEMBRANE_ASSEMBLY −113.44 −2.11 25 10 0.4GO_ALCOHOL_TRANSMEMBRANE_TRANSPORTER_ACTIVITY −135.7 −2.11 24 5 0.21LEUKOCYTE_ACTIVATION −21.62 −2.1 69 16 0.23GO_POSITIVE_REGULATION_OF_PEPTIDYL_SERINE_(—) −60.49 −2.1 88 29 0.33PHOSPHORYLATION GO_OXALATE_TRANSPORT −81.43 −2.09 12 3 0.25GO_MEMBRANE_BIOGENESIS −92.93 −2.09 30 12 0.4 GO_SECRETORY_VESICLE−133.02 −2.09 461 143 0.31 REACTOME_EXTRACELLULAR_MATRIX_ORGANIZATION−29.23 −2.08 87 25 0.29 Targets of ATargets of 01 −45.86 −2.08 259 1090.42 ATPASE_ACTIVITY_COUPLED_TO_MOVEMENT_OF_(—) −130.08 −2.08 40 14 0.35SUBSTANCES GO_ENDOPLASMIC_RETICULUM_CHAPERONE_COMPLEX −3.32 −2.07 11 80.73 GO_CIS_GOLGI_NETWORK −25.98 −2.07 40 23 0.57GO_RESPONSE_TO_OXIDATIVE_STRESS −58.41 −2.07 352 165 0.47 Targets ofFOXD3_01 −77.69 −2.07 199 45 0.23HYDROLASE_ACTIVITY_HYDROLYZING_O_GLYCOSYL_(—) −37.78 −2.06 37 13 0.35COMPOUNDS Targets of CEBP_Q2_01 −52.02 −2.06 267 76 0.28GO_REGULATION_OF_CELL_JUNCTION_ASSEMBLY −53.46 −2.06 68 27 0.4GO_PEPTIDASE_ACTIVITY −53.89 −2.06 663 202 0.3GO_REGULATION_OF_EPITHELIAL_CELL_APOPTOTIC_(—) −88.51 −2.06 59 20 0.34PROCESS ACTIVE_TRANSMEMBRANE_TRANSPORTER_ACTIVITY −98.29 −2.06 122 310.25 GO_REGULATION_OF_PEPTIDASE_ACTIVITY −127.28 −2.06 392 176 0.45GO_RESPONSE_TO_FOOD −15.02 −2.05 19 5 0.26 GO_PROTEIN_DEGLYCOSYLATION−21.99 −2.05 21 13 0.62 GO_AMINOGLYCAN_CATABOLIC_PROCESS −66.41 −2.05 6827 0.4 INTEGRAL_TO_ORGANELLE_MEMBRANE −12.43 −2.04 50 27 0.54LYMPHOCYTE_ACTIVATION −16.18 −2.04 61 15 0.25 BIOCARTA_VITCB_PATHWAY−23.55 −2.04 11 6 0.55 NEGATIVE_REGULATION_OF_SECRETION −25.56 −2.04 135 0.38 MEMBRANE_LIPID_METABOLIC_PROCESS −61.37 −2.04 101 55 0.54GO_CELL_CELL_CONTACT_ZONE −91.65 −2.04 64 21 0.33KEGG_COMPLEMENT_AND_COAGULATION_CASCADES −112.22 −2.04 69 28 0.41GO_NEGATIVE_REGULATION_OF_WOUND_HEALING −182.92 −2.04 58 13 0.22NUCLEOTIDE_KINASE_ACTIVITY −0.4 −2.03 13 5 0.38 GO_ENDODERM_FORMATION−52.71 −2.03 50 20 0.4 GO_GLYCOLIPID_BIOSYNTHETIC_PROCESS −58.12 −2.0362 33 0.53 M1_MACROPHAGES −77.24 −2.03 54 25 0.46 RESPONSE_TO_WOUNDING−137.23 −2.03 190 58 0.31 GO_REGULATION_OF_ASTROCYTE_DIFFERENTIATION−149.91 −2.03 27 7 0.26 GO_HOST −4.29 −2.02 12 8 0.67GO_REGULATION_OF_CHOLESTEROL_HOMEOSTASIS −29.21 −2.02 11 4 0.36GO_REGULATION_OF_SODIUM_ION_TRANSMEMBRANE_(—) −67.2 −2.02 48 14 0.29TRANSPORT TIL_HCC_C9_CD4_GZMK −75.21 −2.02 10 5 0.5 SUGAR_BINDING −98.2−2.02 34 7 0.21 GO_APICAL_PLASMA_MEMBRANE −139.37 −2.02 292 74 0.25GO_REGULATION_OF_SODIUM_ION_TRANSPORT −143 −2.02 77 22 0.29GO_UDP_GLYCOSYLTRANSFERASE_ACTIVITY −33.38 −2.01 139 38 0.27GO_OXIDOREDUCTASE_ACTIVITY_ACTING_ON_THE_(—) −37.81 −2.01 19 6 0.32CH_NH2_GROUP_OF_DONORS GO_ENDODERM_DEVELOPMENT −49.7 −2.01 71 21 0.3GO_CARBOHYDRATE_BINDING −65.62 −2.01 277 72 0.26 Targets of OCT1_Q5_01−69.32 −2.01 273 64 0.23 GO_MATERNAL_PROCESS_INVOLVED_IN_FEMALE_(—)−72.71 −2.01 60 21 0.35 PREGNANCYGO_SODIUM_POTASSIUM_EXCHANGING_ATPASE_(—) −141.23 −2.01 11 3 0.27COMPLEX HALLMARK_COAGULATION −166.89 −2.01 138 64 0.46SULFURIC_ESTER_HYDROLASE_ACTIVITY −49.62 −2 16 4 0.25 GO_RESPONSE_TO_UV39.84 2 126 60 0.48 FATTY_ACID_OXIDATION 17.45 2 18 12 0.67GO_PROTEIN_SUMOYLATION 74.55 2.01 115 68 0.59GO_POSITIVE_REGULATION_OF_DNA_REPAIR 59.89 2.01 38 17 0.45GO_CHROMOSOMAL_REGION 54.33 2.01 330 159 0.48GO_NEGATIVE_REGULATION_OF_DEFENSE_(—) 42.57 2.01 18 8 0.44RESPONSE_TO_VIRUS KEGG_LIMONENE_AND_PINENE_DEGRADATION 40.9 2.01 10 70.7 NUCLEAR_HORMONE_RECEPTOR_BINDING 39.05 2.01 28 15 0.54CELLULAR_PROTEIN_COMPLEX_DISASSEMBLY 35.57 2.01 13 7 0.54BIOCARTA_VEGF_PATHWAY 21.87 2.01 29 15 0.52 GO_FILAMENTOUS_ACTIN 8.262.01 20 6 0.3 GO_DNA_METHYLATION_OR_DEMETHYLATION 2.5 2.01 59 22 0.37GO_REGULATION_OF_TELOMERASE_ACTIVITY 68.25 2.02 43 17 0.4GO_HORMONE_RECEPTOR_BINDING 23.76 2.02 168 73 0.43GO_REGULATION_OF_MITOCHONDRIAL_OUTER_(—) 17.67 2.02 43 23 0.53MEMBRANE_PERMEABILIZATION_INVOLVED_(—) IN_APOPTOTIC_SIGNALING_PATHWAYGO_DNA_HELICASE_COMPLEX 50.82 2.03 14 9 0.64 GO_VIRAL_GENOME_REPLICATION28.4 2.03 21 13 0.62 GO_REGULATION_OF_SPINDLE_ASSEMBLY 8.3 2.03 15 110.73 TAAYNRNNTCC_UNKNOWN 3.81 2.03 172 44 0.26GO_REGULATION_OF_TELOMERE_MAINTENANCE_(—) 108.58 2.04 50 26 0.52VIA_TELOMERE_LENGTHENING BIOCARTA_EIF2_PATHWAY 19.87 2.04 11 7 0.64GO_REGULATION_OF_CHROMATIN_SILENCING 70.9 2.05 21 9 0.43 GO_MICROTUBULE70.74 2.05 405 173 0.43 GO_POSITIVE_REGULATION_OF_PROTEIN_(—) 60.95 2.05129 53 0.41 LOCALIZATION_TO_NUCLEUSGO_NEGATIVE_REGULATION_OF_TELOMERE_(—) 59.17 2.05 17 12 0.71MAINTENANCE_VIA_TELOMERE_LENGTHENING Targets of E2F_Q6_01 44.57 2.05 240111 0.46 PROTEIN COMPLEX DISASSEMBLY 32.97 2.05 14 7 0.5GO_PEROXISOME_PROLIFERATOR_ACTIVATED_(—) 23 2.05 15 4 0.27RECEPTOR_BINDING GO_FEMALE_MEIOTIC_DIVISION 19.4 2.05 26 10 0.38GO_POSITIVE_REGULATION_OF_MRNA_PROCESSING 7.84 2.05 32 20 0.62GO_MICROTUBULE_CYTOSKELETON_ORGANIZATION 7.79 2.05 348 134 0.39 Targetsof AP4_Q6_01 21.61 2.06 255 71 0.28REACTOME_TRANSPORT_OF_MATURE_MRNA_DERIVED_(—) 36.77 2.07 33 26 0.79FROM_AN_INTRONLESS_TRANSCRIPT AUXILIARY_TRANSPORT_PROTEIN_ACTIVITY 9.032.07 26 6 0.23 GO_POSITIVE_REGULATION_OF_TELOMERE_(—) 96.96 2.08 33 140.42 MAINTENANCE_VIA_TELOMERE_LENGTHENINGGO_NEGATIVE_REGULATION_OF_CHROMOSOME_(—) 77.33 2.08 96 49 0.51ORGANIZATION RNA_DEPENDENT_ATPASE_ACTIVITY 48.48 2.08 18 14 0.78GO_MIRNA_BINDING 31.2 2.08 16 5 0.31 GO_G1_DNA_DAMAGE_CHECKPOINT 31.152.08 73 44 0.6 GO_TELOMERE_ORGANIZATION 47.03 2.09 104 49 0.47DNA_INTEGRITY_CHECKPOINT 25.86 2.09 24 11 0.46GO_CYTOPLASMIC_MICROTUBULE 33.85 2.1 57 27 0.47GO_UBIQUITIN_LIKE_PROTEIN_LIGASE_BINDING 27.06 2.1 264 154 0.58GO_POSITIVE_REGULATION_OF_ERYTHROCYTE_(—) 12.02 2.1 23 7 0.3DIFFERENTIATION GO_REGULATION_OF_HISTONE_H3_K9_ACETYLATION 27.95 2.11 144 0.29 GO_DNA_BINDING_BENDING 19.1 2.11 20 6 0.3GO_MACROPHAGE_ACTIVATION_INVOLVED_IN_(—) 10.16 2.11 11 3 0.27IMMUNE_RESPONSE NEGATIVE_REGULATION_OF_IMMUNE_SYSTEM_PROCESS 8.83 2.1114 3 0.21 GO_DNA_INTEGRITY_CHECKPOINT 37.37 2.12 146 72 0.49GO_REGULATION_OF_SPINDLE_ORGANIZATION 15.42 2.12 20 14 0.7GO_CHROMATIN_BINDING 78.09 2.13 435 148 0.34 GO_VIRAL_LATENCY 68.81 2.1311 9 0.82 DNA_HELICASE_ACTIVITY 52.6 2.13 25 15 0.6GO_NUCLEAR_CHROMOSOME_TELOMERIC_REGION 62.34 2.14 132 66 0.5GO_POSITIVE_REGULATION_OF_GLUCOSE_IMPORT_(—) 13.76 2.14 12 4 0.33IN_RESPONSE_TO_INSULIN_STIMULUS GO_CELL_CELL_RECOGNITION 92.12 2.15 6013 0.22 GO_RIBONUCLEOPROTEIN_GRANULE 90.49 2.15 148 87 0.59CONTRACTILE_FIBER_PART 82.69 2.15 23 8 0.35 GO_MITOTIC_NUCLEAR_DIVISION44.49 2.15 361 187 0.52 GO_CELL_CYCLE_PHASE_TRANSITION 35.91 2.16 255127 0.5 Targets of OCT1_02 29.34 2.16 214 50 0.23GO_BINDING_OF_SPERM_TO_ZONA_PELLUCIDA 99.01 2.17 33 9 0.27GO_POSITIVE_REGULATION_OF_DNA_(—) 81.19 2.17 59 23 0.39BIOSYNTHETIC_PROCESS TRANSLATION_FACTOR_ACTIVITY_NUCLEIC_(—) 69.02 2.1739 29 0.74 ACID_BINDING REACTOME_CELL_DEATH_SIGNALLING_VIA_(—) 32.082.17 60 22 0.37 NRAGE_NRIF_AND_NADE GO_EMBRYONIC_HEMOPOIESIS 17.18 2.1720 6 0.3 GO_POSITIVE_REGULATION_OF_TELOMERE_(—) 98.25 2.18 47 24 0.51MAINTENANCE GO_ADENYL_NUCLEOTIDE_BINDING 83.66 2.19 1514 548 0.36GO_DAMAGED_DNA_BINDING 66.18 2.19 63 38 0.6 GO_SPINDLE_POLE 31.43 2.19126 54 0.43 GO_CENTROSOME_CYCLE 7.5 2.19 45 18 0.4 CONTRACTILE_FIBER88.84 2.2 25 8 0.32 AEROBIC_RESPIRATION 53.17 2.2 15 13 0.87RESPONSE_TO_RADIATION 39 2.2 60 16 0.27 PID_IL3_PATHWAY 9.65 2.2 27 100.37 GO_TRANSCRIPTION_EXPORT_COMPLEX 52.59 2.21 13 12 0.92GO_POSITIVE_REGULATION_OF_DNA_TEMPLATED_(—) 47.81 2.21 23 16 0.7TRANSCRIPTION_ELONGATION PID_INSULIN_GLUCOSE_PATHWAY 18.14 2.22 26 110.42 GO_POSITIVE_REGULATION_OF_MRNA_METABOLIC_(—) 17.19 2.22 45 27 0.6PROCESS ZF-MIZ 11.83 2.22 7 4 0.57 GO_MRNA_3_UTR_BINDING 52.16 2.23 4826 0.54 REACTOME_PURINE_METABOLISM 48.02 2.23 33 22 0.67DNA_REPLICATION_INITIATION 9.15 2.23 16 7 0.44GO_REGULATION_OF_CHROMATIN_ORGANIZATION 70.88 2.24 152 61 0.4GO_NEGATIVE_REGULATION_OF_GENE_SILENCING 42.63 2.24 19 5 0.26BIOCARTA_G1_PATHWAY 41.82 2.24 28 10 0.36 GO_CELL_CYCLE_CHECKPOINT 54.942.25 194 93 0.48 GO_PROTEIN_N_TERMINUS_BINDING 25.88 2.25 103 64 0.62GO_ENDODEOXYRIBONUCLEASE_ACTIVITY 48.57 2.26 51 21 0.41GO_ASPARTATE_METABOLIC_PROCESS 31.42 2.26 11 5 0.45GO_POSITIVE_REGULATION_OF_CELLULAR_(—) 16.99 2.26 23 6 0.26RESPONSE_TO_INSULIN_STIMULUS GO_RESPONSE_TO_ACIDIC_PH 16.79 2.26 21 50.24 GO_ENDOLYSOSOME_MEMBRANE 16.29 2.27 11 5 0.45 GO_MYOFILAMENT 82.742.28 24 6 0.25 GO_REGULATION_OF_SIGNAL_TRANSDUCTION_BY_(—) 53.98 2.28162 73 0.45 P53_CLASS_MEDIATOR MACROMOLECULAR_COMPLEX_DISASSEMBLY 38.212.28 15 8 0.53 PID_P73PATHWAY 17.96 2.28 79 41 0.52GO_RIBONUCLEOTIDE_BINDING 81.21 2.29 1860 694 0.37GO_REGULATION_OF_PROTEIN_ACETYLATION 48.57 2.29 64 27 0.42GO_NEGATIVE_REGULATION_OF_CELL_CYCLE_(—) 44.5 2.29 214 104 0.49 PROCESSGO_MEIOTIC_CELL_CYCLE 22.02 2.3 186 58 0.31GO_ALDEHYDE_CATABOLIC_PROCESS 17.84 2.3 13 9 0.69M_PHASE_OF_MITOTIC_CELL_CYCLE 46.52 2.31 85 47 0.55 PID_CMYB_PATHWAY41.73 2.31 84 36 0.43 REACTOME_DOUBLE_STRAND_BREAK_REPAIR 40.26 2.31 249 0.38 REGULATION_OF_MITOSIS 40.4 2.32 41 20 0.49GO_CELL_CYCLE_G2_M_PHASE_TRANSITION 28.36 2.32 138 77 0.56TCCCRNNRTGC_UNKNOWN 23.51 2.32 213 111 0.52 GO_NUCLEAR_CHROMOSOME 70.812.33 523 222 0.42 GO_CHROMATIN_DNA_BINDING 69.13 2.33 80 35 0.44 Targetsof COUP_DR1_Q6 66.25 2.33 247 94 0.38ATP_DEPENDENT_DNA_HELICASE_ACTIVITY 62.62 2.33 11 8 0.73GO_MITOTIC_DNA_INTEGRITY_CHECKPOINT 39.42 2.33 100 56 0.56GO_PROTEIN_C_TERMINUS_BINDING 25.24 2.33 186 81 0.44 GO_P53_BINDING85.62 2.34 67 23 0.34 M_PHASE 45.04 2.35 114 55 0.48GO_CORONARY_VASCULATURE_DEVELOPMENT 30.33 2.35 37 9 0.24GO_NEGATIVE_REGULATION_OF_DNA_DEPENDENT_(—) 9.47 2.35 16 5 0.31DNA_REPLICATION Targets of E2F1_Q4_01 60.41 2.36 228 90 0.39MICROTUBULE_CYTOSKELETON_ORGANIZATION_(—) 50.93 2.36 35 18 0.51AND_BIOGENESIS GO_NEGATIVE_REGULATION_OF_VIRAL_RELEASE_(—) 21.79 2.37 169 0.56 FROM_HOST_CELL REACTOME_APOPTOSIS_INDUCED_DNA_FRAGMENTATION 13.692.37 13 8 0.62 GO_CHROMOSOME 75.27 2.38 880 390 0.44DNA_DEPENDENT_ATPASE_ACTIVITY 64.91 2.38 22 13 0.59GO_NUCLEOSOMAL_DNA_BINDING 79.26 2.39 30 22 0.73GO_DNA_DOUBLE_STRAND_BREAK_PROCESSING 21.71 2.39 20 9 0.45GO_MICROTUBULE_ORGANIZING_CENTER_ORGANIZATION 15.43 2.39 84 40 0.48Targets of E2F_Q4_01 69.24 2.4 237 100 0.42 GO_ORGANELLE_ASSEMBLY 55.852.4 495 214 0.43 GO_REGULATION_OF_PROTEIN_INSERTION_INTO_(—) 13.8 2.4 2915 0.52 MITOCHONDRIAL_MEMBRANE_INVOLVED_IN_(—)APOPTOTIC_SIGNALING_PATHWAY PID_PI3KCI_AKT_PATHWAY 5.36 2.41 35 16 0.46REACTOME_DESTABILIZATION_OF_MRNA_BY_BRF1 57.41 2.42 17 13 0.76GO_POSITIVE_REGULATION_OF_CHROMATIN_MODIFICATION 52.36 2.42 85 35 0.41HISTONE_METHYLTRANSFERASE_ACTIVITY 26.67 2.42 11 4 0.36REACTOME_PLATELET_SENSITIZATION_BY_LDL 22.83 2.42 16 6 0.38PROTEIN_AMINO_ACID_ADP_RIBOSYLATION 20.94 2.42 10 3 0.3PROTEIN_PHOSPHATASE_TYPE_2A_REGULATOR_ACTIVITY 37.57 2.43 14 7 0.5CONDENSED_CHROMOSOME 47.3 2.44 34 16 0.47 GTTRYCATRR_UNKNOWN 16.7 2.44172 45 0.26 MITOCHONDRIAL_TRANSPORT 44.92 2.45 21 19 0.9REACTOME_INTEGRATION_OF_PROVIRUS 80.22 2.46 16 6 0.38GO_POSITIVE_REGULATION_OF_MRNA_SPLICING_VIA_(—) 46.81 2.46 14 6 0.43SPLICEOSOME GO_NEGATIVE_REGULATION_OF_MITOTIC_CELL_CYCLE 36.32 2.46 199100 0.5 ST_FAS_SIGNALING_PATHWAY 29.53 2.46 65 31 0.48GO_POSITIVE_REGULATION_OF_DNA_REPLICATION 68.55 2.47 86 31 0.36GO_NEGATIVE_REGULATION_OF_DNA_REPLICATION 64.35 2.47 55 25 0.45RRCCGTTA_UNKNOWN 36.84 2.47 87 52 0.6 GO_CHROMATIN 66.61 2.48 441 1680.38 GO_RESPONSE_TO_FUNGICIDE 17.51 2.48 11 4 0.36GO_GLOBAL_GENOME_NUCLEOTIDE_EXCISION_REPAIR 16.24 2.49 32 25 0.78GO_DNA_CATABOLIC_PROCESS 16.38 2.5 27 13 0.48GO_ATP_DEPENDENT_DNA_HELICASE_ACTIVITY 54.77 2.51 34 19 0.56MRNA_BINDING 90.95 2.52 23 17 0.74 PID_AURORA_B_PATHWAY 31.45 2.52 39 190.49 CELL_CYCLE_PHASE 53 2.53 170 78 0.46 GO_AU_RICH_ELEMENT_BINDING29.36 2.54 23 12 0.52 GO_REGULATION_OF_MICROTUBULE_POLYMERIZATION_(—)19.11 2.54 178 88 0.49 OR_DEPOLYMERIZATION GO_SUMO_BINDING 13.84 2.54 145 0.36 Targets of CEBPGAMMA_Q6 46.47 2.55 257 78 0.3 HMG 13.08 2.55 5117 0.33 GO_REGULATION_OF_PROTEIN_PHOSPHATASE_TYPE_(—) 33.51 2.57 24 110.46 2A_ACTIVITY KEGG_BETA_ALANINE_METABOLISM 64.25 2.58 22 11 0.5GO_RNA_POLYMERASE_II_DISTAL_ENHANCER_(—) 55.57 2.59 65 28 0.43SEQUENCE_SPECIFIC_DNA_BINDING GO_PEPTIDYL_AMINO_ACID_MODIFICATION 43.762.59 841 340 0.4 GO_NEGATIVE_REGULATION_OF_TELOMERASE_ACTIVITY 29.052.59 15 7 0.47 Targets of AP2REP_01 27.21 2.61 178 57 0.32GO_MITOTIC_SPINDLE_ORGANIZATION 21.78 2.61 69 32 0.46KEGG_GLYOXYLATE_AND_DICARBOXYLATE_METABOLISM 60.42 2.62 16 10 0.62GO_MITOTIC_CELL_CYCLE_CHECKPOINT 53.4 2.62 139 75 0.54GO_REGULATION_OF_CELL_CYCLE_ARREST 50.41 2.62 108 52 0.48GO_REGULATION_OF_DNA_TEMPLATED_TRANSCRIPTION_(—) 46.41 2.62 44 25 0.57ELONGATION GO_RESPONSE_TO_AMMONIUM_ION 32.19 2.62 51 11 0.22GO_REGULATION_OF_THYMOCYTE_APOPTOTIC_PROCESS 49.18 2.63 12 4 0.33GO_POSITIVE_REGULATION_OF_MITOCHONDRIAL_OUTER_(—) 19.54 2.63 36 19 0.53MEMBRANE_PERMEABILIZATION_INVOLVED_IN_APOPTOTIC_(—) SIGNALING_PATHWAYGO_NEGATIVE_REGULATION_OF_TELOMERE_MAINTENANCE 62.11 2.64 26 17 0.65GO_CHROMOSOME_TELOMERIC_REGION 64.33 2.65 162 79 0.49GO_REGULATION_OF_GENE_SILENCING 48.57 2.65 52 16 0.31 PID_ATM_PATHWAY33.28 2.66 34 12 0.35 REACTOME_E2F_ENABLED_INHIBITION_OF_PRE_(—) 17.782.66 10 6 0.6 REPLICATION_COMPLEX_FORMATIONGO_REGULATION_OF_EXECUTION_PHASE_OF_APOPTOSIS 88.61 2.67 24 11 0.46MICROTUBULE 51.63 2.67 32 22 0.69 BIOCARTA_ATRBRCA_PATHWAY 37.5 2.67 218 0.38 GO_NEGATIVE_REGULATION_OF_RESPONSE_TO_BIOTIC_(—) 20.91 2.68 30 140.47 STIMULUS GO_POSITIVE_REGULATION_OF_PROTEIN_IMPORT_INTO_(—) 7.782.69 13 5 0.38 NUCLEUS_TRANSLOCATIONGO_NEGATIVE_REGULATION_OF_EPITHELIAL_CELL_(—) 6.17 2.7 53 21 0.4MIGRATION Targets of E2F1_Q6_01 71.47 2.71 238 98 0.41GO_ORGANIC_ACID_BINDING 25.77 2.71 209 68 0.33 GO_AMINO_ACID_BINDING78.19 2.73 108 36 0.33 MITOTIC_SPINDLE_ORGANIZATION_AND_BIOGENESIS 41.32.73 10 5 0.5 CHROMOSOMEPERICENTRIC_REGION 29.45 2.74 31 14 0.45GO_REGULATION_OF_DNA_REPLICATION 86.77 2.75 161 66 0.41YAATNRNNNYNATT_UNKNOWN 70.49 2.75 104 27 0.26GO_LYMPHOID_PROGENITOR_CELL_DIFFERENTIATION 77.56 2.78 11 3 0.27 Targetsof E2F_Q3_01 60.66 2.79 235 89 0.38 PID_P38_MK2_PATHWAY 31.15 2.82 21 120.57 REACTOME_RECRUITMENT_OF_NUMA_TO_MITOTIC_(—) 26.59 2.82 10 7 0.7CENTROSOMES DNA_RECOMBINATION 70.64 2.85 47 18 0.38GO_GLYOXYLATE_METABOLIC_PROCESS 55.35 2.86 28 14 0.5MITOTIC_CELL_CYCLE_CHECKPOINT 22.04 2.86 21 10 0.48 Targets of EFC_Q617.64 2.86 268 84 0.31 Targets of E2F_Q3 45.09 2.87 227 91 0.4REACTOME_E2F_MEDIATED_REGULATION_OF_DNA_(—) 40.93 2.87 35 13 0.37REPLICATION Targets of ER_Q6_02 15.46 2.87 252 79 0.31GO_POSITIVE_REGULATION_OF_PROTEIN_ACETYLATION 43.79 2.88 36 12 0.33CELL_CYCLE_PROCESS 52.7 2.89 193 87 0.45 Targets of E2F1_Q6 63.66 2.9232 101 0.44 GO_MODULATION_BY_SYMBIONT_OF_HOST_CELLULAR_(—) 8.45 2.92 2811 0.39 PROCESS REACTOME_EARLY_PHASE_OF_HIV_LIFE_CYCLE 76.06 2.94 21 100.48 SPINDLE_POLE 22.59 2.94 18 9 0.5GO_POSITIVE_REGULATION_OF_PROTEIN_EXPORT_(—) 44.14 2.95 19 7 0.37FROM_NUCLEUS GO_GTPASE_ACTIVATING_PROTEIN_BINDING 21.58 2.95 14 7 0.5TRANSCRIPTION_ELONGATION_REGULATOR_ACTIVITY 23.11 2.99 12 7 0.58GO_POSITIVE_REGULATION_OF_DNA_METABOLIC_(—) 102.41 3.01 185 76 0.41PROCESS KEGG_BUTANOATE_METABOLISM 29.15 3.01 34 17 0.5GO_NUCLEAR_CHROMATIN 52.27 3.03 291 111 0.38GO_REGULATION_OF_MICROTUBULE_BASED_PROCESS 24.01 3.04 243 106 0.44GO_FOLIC_ACID_BINDING 53.44 3.06 14 3 0.21 Targets of E2F1DP1RB_01 65.743.1 231 96 0.42 Targets of E2F4DP1_01 60.9 3.1 239 100 0.42BIOCARTA_RB_PATHWAY 33.67 3.1 13 7 0.54GO_POSITIVE_REGULATION_OF_PROTEIN_IMPORT 26.61 3.11 104 35 0.34SGCGSSAAA_Targets of E2F1DP2_01 57.9 3.12 168 77 0.46SPINDLE_ORGANIZATION_AND_BIOGENESIS 51.5 3.13 11 6 0.55 Targets ofE2F1DP1_01 71.16 3.17 235 97 0.41GO_POSITIVE_REGULATION_OF_NUCLEOCYTOPLASMIC_(—) 29.3 3.19 121 40 0.33TRANSPORT REACTOME_TGF_BETA_RECEPTOR_SIGNALING_IN_(—) 66.6 3.2 16 6 0.38EMT_EPITHELIAL_TO_MESENCHYMAL_TRANSITION BIOCARTA_TEL_PATHWAY 35.54 3.2118 10 0.56 Targets of E2F1DP2_01 71.9 3.22 235 97 0.41DNA_DAMAGE_RESPONSESIGNAL_TRANSDUCTION 42.9 3.24 35 13 0.37 Targets ofE2F_02 70.15 3.28 235 98 0.42 BIOCARTA_CHREBP2_PATHWAY 19.81 3.28 42 170.4 PID_BARD1_PATHWAY 56.99 3.32 29 15 0.52GO_NEGATIVE_REGULATION_OF_ORGANELLE_(—) 54.33 3.34 387 184 0.48ORGANIZATION REACTOME_MITOTIC_G2_G2_M_PHASES 45.21 3.36 81 47 0.58Targets of E2F4DP2_01 72.15 3.4 235 97 0.41DNA_DAMAGE_RESPONSESIGNAL_TRANSDUCTION_(—) 39.26 3.44 13 7 0.54BY_P53_CLASS_MEDIATOR REACTOME_TGF_BETA_RECEPTOR_SIGNALING_(—) 40.843.46 26 12 0.46 ACTIVATES_SMADS Targets of E2F1_Q3 79.97 3.47 244 97 0.4NEGATIVE_REGULATION_OF_ANGIOGENESIS 107.96 3.51 13 3 0.23 Targets ofCMYB_01 41.11 3.52 249 106 0.43 GO_RNA_CAP_BINDING_COMPLEX 25.05 3.54 146 0.43 PROTEIN_N_TERMINUS_BINDING 65.41 3.56 38 22 0.58 GO_PRONUCLEUS49.72 3.57 15 9 0.6 PID_DNA_PK_PATHWAY 69.37 3.63 16 9 0.56GO_RESPONSE_TO_COBALT_ION 77.24 3.64 13 7 0.54 GGAMTNNNNNTCCY_UNKNOWN108.65 3.67 117 41 0.35 Targets of SMAD3_Q6 25.73 3.74 239 56 0.23Targets of E2F_Q4 70.57 3.77 234 99 0.42REACTOME_LOSS_OF_NLP_FROM_MITOTIC_(—) 64.59 3.84 59 34 0.58 CENTROSOMESREACTOME_RECRUITMENT_OF_MITOTIC_(—) 67.72 3.9 66 39 0.59CENTROSOME_PROTEINS_AND_COMPLEXES Targets of E2F_Q6 72.88 3.99 232 970.42 Targets of MYCMAX_B 138.78 4.02 268 108 0.4 (Myc and MAX targets)GO_NEGATIVE_REGULATION_OF_ENDOTHELIAL_(—) 13.25 4.42 39 16 0.41CELL_MIGRATION GO_RESPONSE_TO_ARSENIC_CONTAINING_(—) 68.55 4.46 29 180.62 SUBSTANCE GO_REGULATION_OF_CIRCADIAN_RHYTHM 93.03 5.08 103 29 0.28GO_ENDODEOXYRIBONUCLEASE_ACTIVITY_(—) 26.73 5.36 12 4 0.33PRODUCING_5_PHOSPHOMONOESTERS

TABLE S10 Signatures that were used as alternative ICR predictors.Description Signature name Reference AXL (Tirosh) Tirosh et al Science2016 Melanoma cell cycle (Tirosh) Tirosh et al Science 2016 G1 S(Tirosh) Tirosh et al Science 2016 G2 M (Tirosh) Tirosh et al Science2016 Melanoma cells (Tirosh) Tirosh et al Science 2016 MITF (Tirosh)Tirosh et al Science 2016 TME B cell Tumor microenvironment (TME):Current study TME CAF TME: Current study TME Endo TME: Current study TMEMal TME: Current study TME NK TME: Current study TME Neutrophil TME:Current study TME T cells TME: Current study TME T CD4 TME: Currentstudy TME T CD8 TME: Current study TME Macrophage TME: Current study TMEimmune cells TME: Current study TME lymphocytes TME: Current study TMEmeyloid TME: Current study TME stroma TME: Current study Fluidgm Panel Awww.fluidigm.com/applications/advanta-immuno-oncology-gene-expression-assay Fluidgm Panel Bwww.fluidigm.com/applications/advanta-immuno-oncology-gene-expression-assay in-vivo screen GVAXPD1 vs TCRaKO depleted Manguso etal. Cell 2017 in-vivo screen GVAX vs TCRaKO depleted Manguso et al. Cell2017 in-vivo screen TCRaKO vs in-vitro depleted Manguso et al. Cell 2017in-vivo screen GVAXPD1 vs TCRaKO enriched Manguso et al. Cell 2017in-vivo screen GVAX vs TCRaKO enriched Manguso et al. Cell 2017 in-vivoscreen TCRaKO vs in-vitro enriched Manguso et al. Cell 2017 co-culturescreen top 10 hits Patel et al. Nature 2017 co-culture screen top 50hits Patel et al. Nature 2017 Ayers IFNg sig Ayers et al. JCI 2017 Ayersimmune sig Ayers et al. JCI 2017 TME B TME TME TME TME TME T TME T TME TTME TME TME TME TME cell CAP Endo Mal NK cells CD4 CD8 Macrophage immunelymphocytes meyloid stroma ADAM19 ABI3BP A2M ABTB2 CCL4 CXCL13 AQP3APOBEC3C ACP5 ACAP1 ADAM28 ADAP2 ABI3BP ADAM28 ACTA2 ADAM15 ACN9 CD244CST7 CCR4 APOBEC3G ACRBP ADAM28 APOBEC3G AIF1 ACTA2 AFF3 ADAM12 ADCY4ACSL3 CST7 RARRES3 CCR8 CBLB ADAMDEC1 ADAP2 BANK1 AMICA1 ADAM12 BANK1ADAMTS2 AFAP1L1 AHCY CTSW KLRC4 CD28 CCL4 ADAP2 AFF3 BCL11A BCL2A1 ADCY4BCL11A ANTXR1 AQP1 AIF1L GNLY EMB CD4 CCL4L1 ADORA3 AIF1 BCL11B C1orf162AFAP1L1 BIRC3 ASPN ARHGEF15 AK2 GZMA TESPA1 CD40LG CCL4L2 ALDH2 AKNABIRC3 C1QA APP BLK AXL CALCRL ALX1 GZMB LAT CD5 CCL5 ANKRD22 ALOX5 BLKC1QB AQP1 BLNK BGN CCL14 ANKRD54 HOPX CD28 DGKA CD8A C1QA ALOX5AP BLNKC1QC ARHGAP29 BTLA C1R CD200 AP1S2 ID2 IL2RG FAAH2 CD8B C1QB AMICA1 CBLBC3AR1 BGN CCR6 C1S CD34 APOA1BP IL2RB DUSP2 FOXP3 CRTAM C1QC ANKRD44CCL4 C5AR1 C1R CCR7 C3 CD93 APOC2 KLRB1 PAG1 ICOS CST7 C1orf162 AOAHCCL4L1 CASP1 C1S CD19 CALD1 CDH5 APOD KLRC1 TRAT1 IL7R CTSW C3AR1APOBEC3G CCL4L2 CCR1 CALCRL CD1C CCDC80 CFI APOE KLRD1 PPP2R5CLOC100128420 CXCL13 CAPG ARHGAP15 CCL5 CD14 CALD1 CD22 CD248 CLDN5ATP1A1 KLRF1 SKAP1 MAL DTHD1 CARD9 ARHGAP30 CD19 CD163 CCDC80 CD24 CDH11CLEC14A ATP1B1 KLRK1 CD96 PASK DUSP2 CASP1 ARHGAP9 CD2 CD33 CD200 CD37CERCAM COL4A2 ATP5C1 NKG7 GPRIN3 PBXIP1 EOMES CCR2 ARHGDIB CD22 CD4CD248 CD79A CKAP4 CRIP2 ATP5G1 PRF1 CDC42SE2 SLAMF1 FCRL6 CD163 ARRB2CD247 CD68 CD34 CD79B COL12A1 CXorf36 ATP5G2 PTGDR GRAP2 SPOCK2 GZMACD300C B2M CD27 CD86 CDH11 CHMP7 COL14A1 CYYR1 ATP6V0E2 SH2D1B GZMM GZMBCD33 BANK1 CD28 CECR1 CDH5 CIITA COL1A1 DARC ATP6V1C1 RGS1 GZMH CD4BCL11A CD37 CLEC4A CFH CLEC17A COL1A2 DOCK6 ATP6V1E1 SLA2 GZMK CD68BCL11B CD3D CLEC7A CFI CNR2 COL3A1 DOCK9 ATP6V1G1 LOC100130231 ID2 CD86BCL2A1 CD3E CPVL CLDN5 COL19A1 COL5A1 ECE1 AZGP1 PDCD1 IFNG CEBPA BIN2CD3G CSF1R CLEC14A CR2 COL5A2 ECSCR BAIAP2 ICOS IKZF3 CECR1 BIRC3 CD5CSF2RA COL12A1 CXCR5 COL6A1 EGFL7 BANCR EVL ITGAE CLEC10A BLK CD52 CSF3RCOL14A1 ELK2AP COL6A2 ELK3 BCAN TC2N JAKMIP1 CLEC5A BLNK CD6 CSTACOL15A1 FAIM3 COL6A3 ELTD1 BCAS3 LAG3 KLRC4 CMKLR1 BTK CD7 CTSB COL1A1FAM129C COL8A1 EMCN BCHE CBLB KLRC4-KLRK1 CPVL C16orf54 CD79A CTSSCOL1A2 FCER2 CREB3L1 ENG BIRC7 LCK KLRD1 CSF1R C1orf162 CD79B CXCL16COL3A1 FCRL1 CTSK EPHB4 BZW2 TTC39C KLRK1 CTSB C1QA CD8A CYBB COL4A1FCRL2 CXCL12 ERG C10orf90 NLRC5 MIR155HG CTSC C1QB CD8B EPSTI1 COL4A2FCRL5 CXCL14 ESAM C11orf31 CD5 NKG7 CTSH C1QC CD96 FAM26F COL5A1 FCRLACYBRD1 FGD5 C17orf89 ASB2 PRF1 CXCL10 C3AR1 CLEC2D FBP1 COL5A2 HLA-DOBCYP1B1 FLT4 C1orf43 PTPN22 RAB27A CXCL9 C5AR1 CST7 FCGR1A COL6A1HLA-DQA2 DCLK1 GALNT18 C1orf85 RAPGEF6 RUNX3 CXCR2P1 CASP1 CTSW FCGR1BCOL6A2 HVCN1 DCN GNG11 C4orf48 TNFRSF9 TARP CYBB CBLB CXCR5 FCGR2ACOL6A3 IGLL1 DPT GPR116 CA14 SH2D2A TNFRSF9 CYP2S1 CCL3 DENND2D FCGR2CCRIP2 IGLL3P ECM2 GPR146 CA8 GPR174 TOX DMXL2 CCL4 DGKA FCGR3B CTGFIGLL5 EFEMP2 HSPG2 CACYBP ITK DNAJC5B CCL4L1 DUSP2 FCN1 CXCL12 IRF8FAM114A1 HYAL2 CAPN3 PCED1B EBI3 CCL4L2 EEF1A1 FGL2 CXorf36 KIAA0125FAT1 ICA1 CBX3 CD247 EPSTI1 CCL5 EZR FPR1 CYBRD1 KIAA0226L FBLN1 ID1CCDC47 DGKA F13A1 CCR1 FAIM3 FPR2 CYR61 LOC283663 FBLN2 IL3RA CCT2 AAK1FAM26F CCR6 FAM129C FPR3 DCHS1 LTB FBLN5 ITGB4 CCT3 SH2D1A FBP1 CD14FCER2 FTH1 DCN MS4A1 FGF7 KDR CCT6A BTN3A2 FCER1G CD163 FCRL1 FTL DOCK6NAPSB FSTL1 LAMA5 CCT8 PTPN7 FCGR1A CD19 FCRLA G0S2 DPT P2RX5 GPR176LDB2 CDH19 UBASH3A FCGR1C CD2 FYN GLUL ECSCR PAX5 GPX8 LOC100505495 CDH3ACAP1 FOLR2 CD22 GNLY GPX1 EFEMP1 PLEKHF2 HSPB6 MALL CDK2 FASLG FPR3CD244 GZMA HCK EFEMP2 PNOC IGFBP6 MMRN1 CELSR2 INPP4B FUCA1 CD247 GZMBHK3 EGFL7 POU2AF1 INHBA MMRN2 CHCHD6 ARAP2 FUOM CD27 GZMK HLA-C EHD2POU2F2 ISLR MYCT1 CITED1 CD3G GATM CD28 HLA-DOB HLA-DMA ELK3 QRSL1ITGA11 NOS3 CLCN7 IL7R GM2A CD300A HOPX HLA-DMB ELN RALGPS2 ITGBL1NOTCH4 CLNS1A 1-Sep GNA15 CD33 HVCN1 HLA-DRB1 ELTD1 SEL1L3 LOX NPDC1CMC2 SCML4 GPBAR1 CD37 ID2 HLA-DRB5 EMCN SNX29P1 LPAR1 PALMD COA6 IKZF3GPR34 CD38 IGLL5 IFI30 ENG SPIB LRP1 PCDH17 COX7A2 GATA3 GPX1 CD3D IKZF3IGSF6 EPAS1 ST6GAL1 LTBP2 PDE2A CRYL1 PIM2 HLA-DMA CD3E IL2RB IL1RNEPHB4 STAG3 LUM PECAM1 CSAG1 NKG7 HLA-DMB CD3G IL32 IL4I1 ERG STAP1MAP1A PLVAP CSAG2 KLRK1 HLA-DPB2 CD4 IL7R IL8 ESAM TCL1A MEG3 PLXND1CSAG3 SIT1 HLA-DRB1 CD48 IRF8 IRF5 FAM114A1 TLR10 MFAP4 PODXL CSAG4 DEF6HLA-DRB5 CD5 ITK KYNU FAP VPREB3 MFAP5 PRCP CSPG4 GZMH HLA-DRB6 CD52JAK3 LAIR1 FBLN1 WDFY4 MIR100HG PTPRB CYC1 LIME1 HMOX1 CD53 KLRB1 LILRA1FBLN2 MMP2 PVRL2 CYP27A1 GZMA IFI30 CD6 KLRC4 LILRA2 FBLN5 MRC2 RAMP2DAAM2 JAK3 IL4I1 CD68 KLRD1 LILRA3 FBN1 MXRA5 RAMP3 DANCR DENND2D IRF5CD69 KLRK1 LILRA6 FGF7 MXRA8 RHOJ DAP3 SEMA4D KCNMA1 CD7 LAG3 LILRB1FHL1 MYL9 ROBO4 DCT SIRPG KYNU CD72 LAT LILRB2 FN1 NID2 S1PR1 DCXRCLEC2D LAIR1 CD74 LCK LILRB3 FSTL1 NUPR1 SDPR DDT CD8B LGALS2 CD79ALOC283663 LILRB4 GNG11 OLFML2B SELP DLGAP1 THEMIS LILRB1 CD79B LTBLRRC25 GPR116 OLFML3 SHROOM4 DLL3 NLRC3 LILRB4 CD83 LY9 LST1 HSPG2 PALLDSLCO2A1 DNAH14 ZAP70 LILRB5 CD86 MAP4K1 LYZ HTRA1 PCDH18 SMAD1 DNAJA4IL12RB1 LIPA CD8A MS4A1 MAFB HYAL2 PCOLCE STOM DSCR8 CTSW MAFB CD8BNAPSB MAN2B1 ID1 PDGFRA TEK DUSP4 MAP4K1 MAN2B1 CD96 NKG7 MFSD1 ID3PDGFRB TGM2 EDNRB IFNG MARCO CDC42SE2 PARP15 MNDA IFITM3 PDGFRL THBDEIF3C SPOCK2 MFSD1 CECR1 PAX5 MPEG1 IGFBP4 PLAC9 TIE1 EIF3D DTHD1 MPEG1CELF2 PCED1B-AS1 MPP1 IGFBP7 PODN TM4SF1 EIF3E APOBEC3G MS4A4A CIITAPDCD1 MS4A4A IL33 PRRX1 TM4SF18 EIF3H PSTPIP1 MS4A6A CLEC2D PLAC8 MS4A6AISLR RARRES2 TMEM255B EIF3L CD2 MS4A7 CLEC4A POU2AF1 MS4A7 KDR RCN3TSPAN18 ENO1 PRF1 MSR1 CLEC7A POU2F2 MSR1 LAMA5 SDC1 TSPAN7 ENO2 BCL11BMTMR14 CORO1A PRDM1 MXD1 LAMB1 SDC2 VWF ENTHD1 PARP8 NAGA CPVL PRF1 NAIPLAMC1 SEC24D ZNF385D ENTPD6 CXCR3 NPC2 CSF1R PTPN7 NCF2 LDB2 SERPINF1ERBB3 CELF2 OAS1 CSF2RA PTPRCAP NINJ1 LHFP SFRP2 ESRP1 CCL5 OLR1 CSF3RPYHIN1 NPC2 LIMA1 SFRP4 ETV4 IL32 PLA2G7 CST7 RHOH NPL LIMS2 SLIT3 ETV5PRKCQ PPT1 CSTA RNF213 PILRA LOX SMOC2 EXOSC4 WIPF1 PTPRO CTSB RPL13PPT1 LOXL2 SPARC EXTL1 GZMK RASSF4 CTSC RPS27 PSAP LPAR1 SPOCK1 FAHD2BATHL1 RGS10 CTSD RPS3A PTAFR LTBP2 SPON1 FAM103A1 ZC3HAV1 RHBDF2 CTSSRPS6 PYCARD LUM SULF1 FAM178B CD7 RNASE6 CTSW RUNX3 RAB20 MAP1B SVEP1FANCL CD3D RNASET2 CXCL16 1-Sep RASSF4 MEG3 TAGLN FARP2 RASGRP1 RTN1CXCR4 SH2D1A RBM47 MFAP4 THBS2 FASN TBC1D10C SDS CXCR5 SH2D2A RGS2 MGPTHY1 FBXO32 TRAF1 SIGLEC1 CYBA SIRPG RNASE6 MMP2 TMEM119 FBXO7 ARHGEF1SLAMF8 CYBB SIT1 RNF130 MXRA8 TPM1 FDFT1 TARP SLC15A3 CYFIP2 SKAP1RNF144B MYCT1 TPM2 FKBP4 SPATA13 SLC6A12 CYTH4 SP140 S100A8 MYL9 VCANFMN1 PCED1B-AS1 SLC7A7 CYTIP SPOCK2 S100A9 NFIB FOXD3 RUNX3 SLCO2B1DENND2D STAP1 SAT1 NID2 FXYD3 CD6 SPINT2 DGKA STAT4 SERPINAl NNMT GAPDHCD8A TFEC DOCK2 TARP SIGLEC1 NPDC1 GAPDHS NELL2 TIFAB DOCK8 TIGITSIGLEC9 OLFML3 GAS2L3 TNFAIP3 TNFSF13 DOK2 TMC8 SIRPB1 PALLD GAS5 IPCEF1TPP1 DOK3 TOX SLAMF8 PCOLCE GAS7 CXCR6 TREM2 DUSP2 VPREB3 SLC7A7 PDGFRAGDF15 ITGAL TYMP EEF1A1 ZAP70 SLCO2B1 PDGFRB GJB1 RHOF VAMP8 EPSTI1 SPI1PDLIM1 GPATCH4 STAT4 VSIG4 EVI2A SPINT2 PLAC9 GPM6B PVRIG ZNF385A EVI2BTBXAS1 PLVAP GPNMB TIGIT EZR TFEC PLXND1 GPR137B CD27 FAIM3 THEMIS2 PODNGPR143 ZNF831 FAM129C TLR2 PODXL GSTP1 RNF213 FAM26F TNFRSF10C PPAP2AGYG2 SYTL3 FAM49B TNFSF13 PPIC H2AFZ CNOT6L FAM65B TPP1 PRCP HIST1H2BDSPN FBP1 TREM1 PRRX1 HIST3H2A GPR171 FCER1G VSIG4 PRSS23 HMG20B AKNAFCER2 ZNF385A PTPRB HMGA1 FYN FCGR1A PTRF HPGD RASAL3 FCGR1B PXDN HPS4CCL4 FCGR2A RAMP2 HPS5 TOX FCGR2C RAMP3 HSP90AA1 PRDM1 FCGR3A RARRES2HSP90AB1 PIP4K2A FCGR3B RCN3 HSPA9 CTLA4 FCN1 RHOJ HSPD1 GZMB FCRL1ROBO4 HSPE1 HNRNPA1P10 FCRLA S100A16 IGSF11 CD3E FERMT3 S1PR1 IGSF3IKZF1 FGD2 SELM IGSF8 JAKMIP1 FGD3 SERPINH1 INPP5F PYHIN1 FGL2 SLCO2A1ISYNA1 MIAT FGR SMAD1 KCNJ13 LEPROTL1 FPR1 SPARC LAGE3 OXNAD1 FPR2SPARCL1 LDHB RAB27A FPR3 SULF1 LDLRAD3 IL2RB FTH1 SYNPO LEF1-AS1 KLRD1FTL TAGLN LHFPL3-AS1 PIK3IP1 FYB TEK LINC00473 FYN TFPI LINC00518 G0S2TGFB1I1 LINC00673 GBP5 THBS1 LOC100126784 GLUL THBS2 LOC100127888 GNA15THY1 LOC100130370 GNLY TIE1 LOC100133445 GPR183 TM4SF1 LOC100505865GPSM3 TMEM204 LOC146481 GPX1 TMEM255B LOC340357 GRB2 TNS1 LONP2 GZMATPM1 LOXL4 GZMB TPM2 LZTS1 GZMK VCL MAGEA1 HAVCR2 VWF MAGEA12 HCK MAGEA2HCLS1 MAGEA2B HCST MAGEA3 HK3 MAGEA4 HLA-B MAGEA6 HLA-C MAGEC1 HLA-DMAMDH1 HLA-DMB MFI2 HLA-DOB MFSD12 HLA-DPA1 MIA HLA-DPB1 MIF HLA-DPB2 MITFHLA-DQA1 MLANA HLA-DQA2 MLPH HLA-DQB1 MOK HLA-DQB2 MRPS21 HLA-DRA MRPS25HLA-DRB1 MRPS26 HLA-DRB5 MRPS6 HLA-G MSI2 HMHA1 MXI1 HOPX MYO10 HVCN1NAV2 ID2 NDUFA4 IFI30 NDUFB9 IGFLR1 NEDD4L IGLL5 NELFCD IGSF6 NHP2 IKZF1NME1 IKZF3 NOP58 IL10RA NPM1 IL16 NSG1 IL1RN NT5DC3 IL2RB OSTM1 IL2RGPACSIN2 IL32 PAGE5 IL4I1 PAICS IL7R PAX3 IL8 PEG10 INPP5D PFDN2 IRF5 PHBIRF8 PHLDA1 ITGAL PIGY ITGAM PIR ITGAX PKNOX2 ITGB2 PLEKHB1 ITK PLP1JAK3 PLXNB3 KLRB1 PMEL KLRC4 POLR2F KLRD1 PPIL1 KLRK1 PPM1H KYNU PRAMELAG3 PSMB4 LAIR1 PUF60 LAPTM5 PYGB LAT PYURF LAT2 QDPR LCK RAB17 LCP1RAB38 LCP2 RAP1GAP LILRA1 RGS20 LILRA2 RNF43 LILRA3 ROPN1 LILRA6 ROPN1BLILRB1 RPL38 LILRB2 RSL1D1 LILRB3 RTKN LILRB4 S100A1 LIMD2 S100B LITAFSCD LOC283663 SDC3 LRRC25 SEC11C LSP1 SEMA3B LST1 SERPINA3 LTB SERPINE2LY86 SGCD LY9 SGK1 LYN SH3D21 LYST SHC4 LYZ SLC19A2 M6PR SLC24A5 MAFBSLC25A13 MAN2B1 SLC25A4 MAP4K1 SLC26A2 1-Mar SLC3A2 MFSD1 SLC45A2 MNDASLC5A3 MPEG1 SLC6A15 MPP1 SLC6A8 MS4A1 SLC7A5 MS4A4A SNCA MS4A6A SNHG16MS4A7 SNHG6 MSR1 SNRPC MXD1 SNRPD1 MYO1F SNRPE NAIP SOD1 NAPSB SORD NCF1SORT1 NCF1B SOX10 NCF1C SOX6 NCF2 SPCS1 NCF4 SPRY4 NCKAP1L ST13 NINJ1ST3GAL4 NKG7 ST3GAL6 NPC2 ST3GAL6-AS1 NPL ST6GALNAC2 PAG1 STIP1 PARP15STK32A PARVG STMN1 PAX5 STX7 PCED1B-AS1 STXBP1 PDCD1 SYNGR1 PIK3AP1TBC1D7 PIK3R5 TBCA PILRA TEX2 PIM2 TFAP2A PION TFAP2C PLAC8 TMEM147PLCB2 TMEM14B PLEK TMEM177 PLEKHA2 TMEM251 POU2AF1 TMX4 POU2F2 TNFRSF21PPT1 TOM1L1 PRDM1 TOMM20 PRF1 TOMM22 PSAP TOMM6 PSMB10 TOMM7 PSTPIP1TOP1MT PTAFR TRIB2 PTK2B TRIM2 PTPN6 TRIM63 PTPN7 TRIM9 PTPRC TRIML2PTPRCAP TRMT112 PYCARD TSPAN10 PYHIN1 TTLL4 RAB20 TTYH2 RAC2 TUBB2BRASSF4 TUBB4A RBM47 TYR RGS1 TYRP1 RGS19 UBL3 RGS2 UQCRH RHOF UTP18 RHOGVAT1 RHOH VDAC1 RNASE6 VPS72 RNASET2 WBSCR22 RNF130 XAGE1A RNF144BXAGE1B RNF213 XAGE1C RPL13 XAGE1D RPS27 XAGE1E RPS3A XYLB RPS6 ZCCHC17RPS6KA1 ZFP106 RUNX3 ZNF280B S100A8 ZNF330 S100A9 SAMHD1 SAMSN1 SASH3SAT1 SCIMP SELL SELPLG 1-Sep SERPINA1 SH2D1A SH2D2A SIGLEC1 SIGLEC14SIGLEC7 SIGLEC9 SIRPB1 SIRPG SIT1 SKAP1 SLA SLAMF6 SLAMF7 SLAMF8 SLC7A7SLCO2B1 SNX10 SP140 SPI1 SPINT2 SPN SPOCK2 SRGN STAP1 STAT4 STK17BSTXBP2 SYK TAGAP TARP TBC1D10C TBXAS1 TFEC THEMIS2 TIGIT TLR1 TLR2 TMC8TNFRSF10C TNFRSF9 TNFSF13 TOX TPP1 TRAF3IP3 TREM1 TYROBP UCP2 VAMP8 VAV1VNN2 VPREB3 VSIG4 WIPF1 ZAP70 ZNF385A

TABLE S11 Signatures of Expanded T cells Up/down regulated in expanded Tcells compared to non-expanded T cells. up (expanded) down (expanded) up(all) down (all) ABCD2 ALOX5AP ABCD2 NAB1 AAK1 MCM5 ADAM28 ANXA1 ADAM28NCALD AHNAK MRPS24 AIM2 ARL4C AIM2 NEK7 ALOX5AP MRPS34 AKAP5 C12orf75AKAP5 NFAT5 ANAPC15 MUTYH AP1AR CAMK4 AKAP8L NMB ANXA1 MXD4 ARID5ACD200R1 ANAPC4 NOD2 AP5S1 MYH9 ARNT CD44 AP1AR NOTCH1 APOBEC3G NDUFB9ATHL1 CD5 AQR NSUN2 ARL4C NEDD8 ATP2C1 COX7A2 ARID5A OPA1 ASF1B NFKBIZBCOR DBF4 ARNT ORMDL3 ATG16L2 NR4A3 CADM1 EMP3 ATHL1 OSBPL3 AURKA NUP37CCL3L3 FAM46C ATM PAPOLA BOLA3 PCK2 DGKD FOSB ATP2C1 PARP11 BUB1 PCNADTHD1 GZMH ATXN7L1 PCED1B C12orf75 PDCD5 ETV1 HMGA1 BCOR PCM1 C3orf38PDE4B G3BP1 KIAA0101 C17orf59 PDE7B CAMK4 PFDN2 HSPA1B KLRG1 C18orf25PDGFD CARD16 PHLDA1 ID3 LIME1 CADM1 PDXDC2P CCR5 POLR2K ITM2A LMNB1 CAV1PIK3AP1 CCR7 PRDX3 KCNK5 MAB21L3 CCL3L3 PIKFYVE CD200R1 PRPF4 KLRC2NR4A3 CD200 PJA2 CD44 PRR5L KLRC3 PCK2 CDC73 PRKCH CD5 PXN KLRC4 PCNACEP85L PROSER1 CD97 RDH11 KLRK1 PDCD5 DDX3Y PSTPIP1 CKS1B REXO2LOC220729 PDE4B DDX6 PTPN6 COX7A2 RNASEH2C LONP2 PFDN2 DGKD PYHIN1 DBF4RNASEK LRBA RDH11 DGKH RALGDS DNAJC9 RPUSD3 LYST S100A10 DNAJA2 RCBTB2DTYMK RTCA NAB1 S100A4 DTHD1 RGS2 ECE1 S100A10 NMB SAMD3 ELF1 RGS4 ECHS1S100A4 PAPOLA SPOCK2 ELMO1 RHOB ELL S100A6 PDE7B TKT ETNK1 RIN3 EMP3S1PR1 PIK3AP1 TNF ETV1 RNF19A F2R SAMD3 PRKCH TOBI FAIM3 RWDD2B FAM46CSELL PROSER1 TOMM7 FBXW11 S100PBP FAM50B SLIRP PTPN6 TUBA1C FCRL3 SATB1FOSB SPOCK2 PYHIN1 UGDH-AS1 FOXN2 SDAD1 FOXP1 STX16 RGS2 G3BP1 SEC24CGMCL1 TANK RGS4 GALT SERINC3 GNPTAB TKT S100PBP GFOD1 SFI1 GPR183TMEM173 SH2D1B GNG4 SH2D1B GTF3C6 TNF SNAP47 HIF1A SKIV2L GYPC TNFAIP3SPDYE8P HIST1H2BG SLC30A7 GZMA TNFSF4 SPRY2 HIST2H2BE SLC7A5P1 GZMH TOBISYVN1 HSPA1B SLFN11 HAUS4 TOMM5 TACO1 HSPB1 SNAP47 HMGA1 TOMM7 THADA ID3SOD1 HMOX2 TPT1 TP53INP1 IL6ST SPATA13 INSIG1 TUBA1B TSC22D1 INPP5BSPDYE8P ITM2C TUBA1C UBA7 INPP5F SPRY2 KIAA0101 TUBB4B ZMYM2 IRF8 STT3BKLF6 TXN ITM2A SYVN1 KLRB1 UBE2Q2P3 KCNK5 TACO1 KLRG1 UCHL3 KDM4CTBC1D23 LEF1 UGDH-AS1 KLRC2 TBC1D4 LIME1 UQCRB KLRC3 THADA LMNB1 VIMKLRC4 TNFRSF9 LTB WBP11 KLRD1 TNIP1 LY6E ZNF683 KLRK1 TP53INP1 MAB21L3LOC100190986 TRAF5 LOC220729 TSC22D1 LOC374443 TTI2 LONP2 TTTY15 LRBATXNDC11 LRRC8D UBA7 LSM14A VMA21 LY9 VPRBP LYST WWC3 MBP ZBED5 MED13ZMYM2 MGA ZMYM5 MGEA5 ZNF384 MS4A1 ZNF468 MST4 ZNF83 NAA16

Example 2—Immunotherapy Resistance Signature from 26 Melanoma Tumors

Applicants performed single-cell RNA-seq on 26 melanoma tumors (12treatment naïve, 14 post immunotherapy) (FIG. 17). Applicants discoveredthat immunotherapy leads to profound transcriptional alterations in boththe malignant and immune cells. Applicants also discovered that thesetranscriptional programs are associated the response to immunotherapy byanalyzing prior data sets (Hugo et al. Cell. 2016 Mar. 24; 165(1):35-44.doi: 10.1016/j.cell.2016.02.065; and Riaz et al. Nature Genetics 48,1327-1329 (2016) doi:10.1038/ng.3677). Applicants also discovered thatthese transcriptional programs are associated Intra-tumor:heterogeneity, location, and antigen presentation. Applicants exploredand characterized the effect immunotherapies have on different celltypes within the tumor (i.e., Malignant cells, CD8/CD4 T-cells, B cellsand Macrophages). The data includes twenty six samples (14 postimmunotherapy, 8 anti-CTLA4 & anti-PD-1, 2 anti-PD1(Nivolumab), 4anti-CTLA4 (Ipilimumab), and 12 treatment naïve (FIG. 17).

Applicants performed principal component analysis on the expressiondata. The second Principle Component (PC) separates betweenimmunotherapy resistant and untreated tumors (FIG. 18). Applicantsdiscovered that treatment is the main source of variation in malignantcells between tumors, reflected by the difference in the score ofmalignant cells from treatment naive and resistant tumors on the secondprinciple component.

Applicants analyzed the transcriptome of the malignant cells to identifycell states that are associated with immunotherapy. To this end,Applicants identified differentially expressed genes and derived twopost-immunotherapy (PIT) modules, consisting of genes that are up(PIT-up) or down (PIT-down) regulated in PIT malignant cells compared tothe untreated ones. In comparison to the treatment naive tumors, all thePIT tumors overexpress the PIT-up module and underexpress the PIT-downmodule, such that there is a spectrum of expression levels also withineach patient group and within the malignant cell population of a singletumor (FIG. 19). The genes within each module are co-expressed, whilethe two modules are anti-correlated with each other, not only acrosstumors but also within the malignant cell population of a single tumor.Additionally, the two modules have heavy and opposite weights in thefirst principle components of the malignant single-cell expressionprofiles, indicating that immunotherapy is one of the main sources ofinter-tumor heterogeneity in the data.

Applicants applied down sampling and cross-validation to confirm thatthe PIT modules are robust and generalizable (FIG. 20). Morespecifically, Applicants repeatedly identified the signatures withoutaccounting for the data of one of the tumors, and showed that themodules were similar to those derived with the full dataset.Furthermore, the modules that were derived based on a training datacould still correctly classify the test tumor as either PIT or treatmentnaive. The signature is very robust. If Applicants leave out all themalignant cells from a given tumor, recalculate it and then assign thecells, Applicants make only one “error” when guessing if the tumor istreatment naive or ITR. This one tumor has a particularly high T cellinfiltration. These results testify that, while more data and sampleswill enable us to refine these modules, the resulting modules are notlikely to change substantially. The signature is also supported by themutual exclusive expression of the up and down genes across malignantcells, and their anti-correlation in TCGA (FIG. 21).

Gene set enrichment analysis of the PIT programs highlightswell-established immune-evasion mechanisms as the down-regulation of MEWclass I antigen presentation machinery and interferon gamma signaling inPIT cells (Table 1). Cells with less MHC-I expression are more resistantto immunotherapy (FIG. 22). Additionally, it has been recently shownthat melanoma tumors that are resistant to ipilimumab therapy containgenomic defects in IFN-gamma pathway genes, and that the knockdown ofIFNGR1 promotes tumor growth and reduces mouse survival in response toanti-CTLA-4 therapy. The PIT-down program is also enriched with genesinvolved in coagulation, IL2-STAT5 signaling, TNFα signaling via NFkB,hypoxia, and apoptosis. The PIT-up program is tightly linked to MYC. Itis enriched with MYC targets and according to the connectivity map data(c-map)—MYC knockout alone is able to repress the expression of theentire PIT-up signature. Supporting these findings, it has been shownthat MYC modulates immune regulatory molecules, such that itsinactivation in mouse tumors enhances the antitumor immune response.Interestingly, Applicants find that metalothionines (MTs) areoverrepresented in the PIT-down program, and show that their expressionalone separates between the PIT and untreated samples (FIG. 23). MTs area family of metal-binding proteins that function as immune modulatorsand zinc regulators. The secretion of MTs to the extracellular matrixcan suppress T-cells and promote T-cell chemotaxis. Interestingly, ithas been recently shown that MT2A is a key regulator of CD8 T-cells,such that its inhibition promotes T-cell functionality in theimmunosuppressive tumor microenvironment (Singer et al. Cell. 2016 Sep.8; 166(6):1500-1511). The underexpression of MTs in the malignant cellsof post-immunotherapy tumors could potentially be linked to the role ofMT2A in T-cells and to the abundance of zinc in the tumormicroenvironment.

TABLE 1 Functional classification of PIT module genes. Pathway Genes MHCclass I antigen presentation machinery CTSB, HLA-A, HLA-C, HLA-E, HLA-F,PSME1, TAP1, TAPBP Coagulation ANXA1, CD9, CFB, CTSB, FN1, ITGB3, LAMP2,PROS1, PRSS23, SERPINE1, SPARC, TF TNFα signaling via NFkB ATF3, BCL6,BIRC3, CD44, EGR1, GADD45B, GEM, JUNB, KLF4, KLF6, NR4A1, PDE4B,SERPINE1, TAP1, TNC IL2/STAT5 signaling AHNAK, AHR, CCND3, CD44, EMP1,GADD45B, Metallothioneins IFITM3, IGF1R, ITGA6, KLF6, NFKBIZ, PRNP, RNH1MT1E, MT1F, MT1G, MT1M, MT1X, MT2A MYC targets EIF4A1, FBL, HDAC2, ILF2,NCBP1, NOLC1, PABPC1, PRDX3, RPS3, RUVBL2, SRSF7

Applicants identified an immunotherapy resistance signature byidentifying genes that were up and down regulated in immunotherapytreated subjects as compared to untreated subjects (Table 2, 3). Thesignature was compared to clinical data of subjects that were completeresponders to immunotherapy, partial responders and non-responders. Thedata was also compared to subjects with high survival and low survival.

TABLE 2 Analysis of all gene expression data and clinical dataclinic.R.more clinic.R.less sc.All sc.Old sc.New sc.Bulk sc.Q.genetcga.Increascd.risk tcga.Increased.risk.beyond.T.cells ANXA1 7.58E−028.19E−01 −202.40 −2.44 −200.00 −3.02 FALSE −2.64 −1.37 EMP1 4.92E−018.96E−02 −189.84 −20.93 −75.82 −2.58 FALSE 0.69 0.60 TSC22D3 4.26E−014.63E−01 −175.14 −13.19 −82.60 −2.43 FALSE −1.52 −0.32 MT2A 4.06E−027.81E−01 −174.76 −18.16 −67.52 −4.41 FALSE −2.83 −1.80 CTSB 4.03E−015.87E−01 −165.96 −25.70 −112.90 −2.50 FALSE −0.44 0.56 TM4SF1 1.76E−017.74E−01 −164.10 5.621836397 −165.5071875 −1.14 FALSE 0.45 0.32 CDH194.35E−02 4.15E−01 −155.59 −3.79 −42.42 −1.53 FALSE −2.24 −1.86 MIA3.62E−01 4.96E−01 −152.98 −4.22 −60.91 −1.53 FALSE −1.60 −0.91 SERPINE22.27E−02 1.70E−01 −151.17 −31.78 −46.03 −1.68 FALSE −3.66 −3.12 SERPINA31.43E−01 4.64E−01 −148.25 13.63 −229.59 −1.37 FALSE −2.64 −2.04 S100A62.91E−01 2.01E−01 −128.57 −12.30 −49.34 −2.42 FALSE −0.40 −0.47 ITGA33.35E−02 9.20E−01 −123.57 1.88215819 −83.80670184 −0.97 FALSE −0.51−0.49 SLC5A3 4.64E−01 4.71E−01 −119.83 1.06 −96.80 −1.71 FALSE −6.19−4.10 A2M 3.01E−02 4.38E−01 −118.06 −15.73720409 −30.29161959 −1.07FALSE −2.81 −1.47 MFI2 3.67E−01 4.22E−01 −117.29 −3.06 −44.01 −1.41FALSE 0.46 0.38 CSPG4 7.50E−01 2.24E−01 −112.90 −5.56 −30.13 −1.87 FALSE−1.60 −1.41 AHNAK 5.70E−02 7.09E−01 −112.83 −12.69 −13.16 −2.03 FALSE−0.45 −0.38 APOC2 6.76E−01 1.57E−01 −111.01 4.108007818 −92.34012794−0.52 FALSE −0.55 0.51 ITGB3 1.66E−01 3.79E−01 −110.25 0.79 −109.99−1.75 FALSE −2.25 −1.42 NNMT 4.47E−01 6.63E−01 −110.12 −1.62 −122.65−2.51 FALSE −2.28 −0.87 ATP1A1 2.34E−01 4.92E−01 −107.58 −19.25 −26.63−1.40 FALSE 0.52 0.30 SEMA3B 8.03E−02 9.69E−01 −106.75 −2.022007432−74.18998319 −1.06 FALSE −1.65 −1.08 CD59 3.34E−02 7.57E−01 −101.92−16.59 −40.13 −1.86 FALSE −1.71 −0.90 PERP 1.03E−01 9.58E−01 −99.65−2.61892627 −123.0851115 −1.13 FALSE 0.78 −0.77 EGR1 1.98E−01 8.54E−01−96.70 −1.43 −25.74 −1.30 FALSE −0.80 0.30 LGALS3 2.66E−01 6.41E−01−96.06 −57.79606403 1.174991366 −1.19 FALSE −0.42 −0.50 SLC26A2 1.86E−012.65E−01 −95.69 0.615403346 −34.74613485 −0.92 FALSE −3.62 −2.73 CRYAB2.26E−02 5.72E−01 −94.74 0.85 −139.66 −1.89 FALSE −0.84 −0.63 HLA-F4.70E−02 9.62E−01 −94.42 −12.84 −23.07 −1.82 FALSE −4.49 −1.03 MT1E1.78E−01 5.89E−01 −92.61 −14.66 −27.25 −3.00 FALSE −1.20 −1.19 KCNN41.88E−01 7.63E−01 −92.09 −1.36 −108.90 −2.56 FALSE −4.61 −2.92 CST31.87E−01 6.36E−01 −90.32 −3.11 −43.51 −2.19 FALSE −1.31 0.32 CD96.23E−01 4.58E−01 −89.32 −9.57 −19.34 −2.77 FALSE 0.35 −0.79 TNC3.59E−01 6.45E−01 −87.60 −6.21 −88.45 −1.78 FALSE −2.72 −1.10 SGCE2.19E−02 3.21E−01 −87.28 −0.302176627 −62.80958661 −1.02 FALSE −3.12−1.69 NFKBIZ 2.32E−02 9.71E−01 −86.67 −4.35 −30.64 −2.89 FALSE −2.40−1.85 PROS1 2.16E−02 4.37E−01 −86.35 −0.52 −28.78 −1.72 FALSE −0.40−0.71 CAV1 6.55E−02 3.13E−01 −85.42 −24.08 −6.13 −1.34 FALSE −1.43 −0.72MFGE8 2.64E−01 3.77E−01 −84.81 −12.26983949 −19.33461436 −1.07 FALSE−1.84 −1.29 IGFBP7 7.97E−01 9.60E−02 −83.96 −22.46 −27.89 −1.37 FALSE−0.37 0.88 SLC39A14 1.73E−01 8.74E−01 −83.65 0.52 −37.30 −1.97 FALSE−0.52 −0.67 CD151 2.53E−01 3.98E−01 −83.63 −2.11 −33.44 −1.90 FALSE−0.56 −0.63 SCCPDH 5.51E−01 3.85E−01 −83.37 −3.18 −20.08 −1.68 FALSE−1.26 −1.07 MATN2 6.66E−01 2.81E−01 −82.90 −0.523529704 −70.36560095−1.17 FALSE −0.34 0.68 DUSP4 2.30E−01 3.73E−01 −82.27 −6.19379111−19.37401872 −1.18 FALSE 0.58 0.60 APOD 3.39E−01 5.42E−01 −81.89 −9.70−15.76 −1.58 FALSE −1.62 −1.49 GAA 1.72E−01 6.87E−01 −81.55 −2.56 −27.32−1.50 FALSE −1.23 −0.56 CD58 1.48E−01 5.02E−01 −81.12 −1.03 −40.89 −2.52FALSE −2.40 −3.24 HLA-E 5.00E−02 9.47E−01 −79.92 −25.19 −23.50 −1.86FALSE −3.54 0.48 TIMP3 4.17E−01 8.28E−02 −79.58 −6.278620205−2.728290317 −1.11 FALSE −1.13 −1.19 NR4A1 1.22E−01 6.52E−01 −79.47−14.82 −8.42 −1.37 FALSE 0.32 −0.51 FXYD3 2.31E−02 8.64E−01 −78.83 −3.88−17.90 −1.81 FALSE −0.47 −0.96 TAPBP 9.56E−02 9.33E−01 −78.23 −9.90−25.67 −1.40 FALSE −3.06 0.45 CTSD 2.10E−01 4.50E−01 −76.29 −35.68−12.15 −1.73 FALSE 0.51 1.21 NSG1 2.25E−01 5.26E−02 −75.54 −6.8850195−45.25690934 −0.59 FALSE NA NA DCBLD2 1.51E−01 3.97E−01 −75.17 −2.70−30.36 −2.50 FALSE −0.93 −1.69 GBP2 3.33E−02 4.65E−01 −74.58 −6.60−112.34 −3.42 FALSE −9.49 −2.53 FAM3C 2.17E−02 3.24E−01 −73.79−1.099557442 −34.2099212 −0.80 FALSE −4.22 −2.95 CALU 7.70E−01 2.45E−01−73.21 −2.96 −22.58 −1.44 FALSE 0.34 −0.56 DDR1 1.30E−02 9.47E−01 −72.941.320302264 −41.93649931 −0.93 FALSE −0.66 −1.98 TIMP1 2.44E−01 1.95E−01−72.66 0.832732502 −44.31465375 −1.27 FALSE −2.53 −0.80 LRPAP1 3.26E−015.82E−01 −72.03 −8.741825947 −33.28409269 −1.12 FALSE 0.55 0.62 CD441.20E−01 7.83E−01 −71.20 −42.03 −7.56 −1.31 FALSE −1.20 −0.70 GSN1.83E−01 9.76E−02 −71.17 −7.066367901 −8.379109684 −1.25 FALSE −0.48−0.40 PTRF 1.20E−01 1.26E−01 −70.87 −11.99 −21.89 −2.19 FALSE −0.81−0.88 CAPG 3.42E−01 4.17E−01 −70.60 −17.12110776 −3.792804113 −1.21FALSE −0.42 0.69 CD47 1.14E−01 8.55E−01 −68.77 −5.84 −21.44 −2.75 FALSE−5.65 −3.19 CCND3 1.48E−01 7.90E−01 −68.60 −0.85 −62.30 −2.43 FALSE−0.65 0.43 HLA-C 1.63E−01 4.28E−01 −68.47 −22.92 −13.18 −1.33 FALSE−4.97 −1.10 CARD16 3.15E−02 9.14E−01 −68.09 −1.20 −51.51 −1.48 FALSE−0.65 0.50 DUSP6 3.52E−01 3.46E−01 −67.35 −1.443530586 −32.17071544−0.53 FALSE −4.33 −2.45 IL1RAP 6.76E−03 4.53E−01 −66.82 −2.25 −24.21−3.64 FALSE −1.77 −1.51 FGFR1 7.25E−02 1.31E−01 −66.47 9.950506533−57.92951091 −1.14 FALSE −0.49 −0.62 TRIML2 8.90E−01 1.20E−01 −66.2421.84557542 −68.40922705 −0.49 FALSE −1.47 −1.52 ZBTB38 7.00E−013.77E−01 −65.84 −6.25 −8.44 −1.64 FALSE −3.18 −3.11 PRSS23 6.42E−018.53E−02 −63.62 −0.34 −35.59 −1.53 FALSE −0.54 0.37 S100B 4.64E−016.74E−01 −63.21 −18.39689161 −0.989534032 −1.08 FALSE −1.72 −0.73 PLP21.29E−02 7.48E−01 −63.01 −3.16 −7.46 −1.46 FALSE 0.34 −0.80 LAMP22.64E−01 6.13E−01 −62.96 −5.73 −13.68 −1.48 FALSE −1.19 −1.06 FCGR2A8.31E−04 8.38E−01 −62.40 −0.623470411 −28.64302633 −0.93 FALSE −6.97−2.82 LGALS1 7.24E−02 1.72E−01 −61.40 −12.41 −1.43 −1.38 FALSE 0.77 1.02NPC1 9.96E−02 4.70E−01 −60.93 −2.330822107 −12.24708172 −0.83 FALSE 0.37−0.31 UBC 6.96E−01 4.80E−01 −60.76 −6.83 −41.63 −1.69 FALSE −1.71 −0.61TNFRSF12A 8.03E−02 7.99E−01 −60.31 1.73 −37.68 −1.53 FALSE −0.63 −0.66SPON2 1.56E−01 2.67E−01 −59.94 −0.444813435 −54.28549635 −0.87 FALSE−0.62 0.47 EEA1 4.38E−01 4.73E−01 −59.50 0.680184335 −13.23401918 −1.02FALSE −1.33 −2.70 CD63 7.00E−01 2.67E−01 −59.49 −14.73233445−14.29263209 −1.30 FALSE 1.10 0.65 SGK1 4.34E−01 3.83E−01 −59.42−2.77588165 −13.5729112 −0.52 FALSE 0.63 0.68 HPCAL1 1.03E−01 6.53E−02−59.22 −8.70 −10.48 −1.83 FALSE −0.69 −0.91 HLA-B 5.22E−02 8.84E−01−58.69 −16.7731158 −7.71200708 −1.18 FALSE −5.85 −0.79 SERPINA1 5.48E−014.51E−01 −58.50 4.67012442 −61.13154453 −0.74 FALSE −2.78 0.47 JUN3.03E−01 7.09E−01 −58.42 1.182777495 −17.43164065 −1.18 FALSE −0.89 0.32HLA-A 4.98E−02 9.30E−01 −58.18 −26.50 −18.12 −1.46 FALSE −2.09 −0.34RAMP1 5.43E−01 2.02E−01 −58.03 −11.93 −63.50 −1.60 FALSE 0.45 0.53 TPP17.54E−02 8.18E−01 −57.91 −18.16810565 −4.426800522 −1.02 FALSE −0.70−0.41 FYB 1.49E−01 7.19E−01 −57.13 −2.867192419 −45.73445912 −0.52 FALSE−4.33 −0.75 RDH5 1.02E−01 8.47E−01 −56.99 1.683618144 −39.48423368 −0.59FALSE −3.18 −2.30 SDC3 1.84E−01 4.92E−01 −56.80 −2.227320442−7.930319849 −0.90 FALSE −1.46 −0.67 PRKCDBP 2.03E−01 3.35E−01 −56.58−3.45 −25.88 −2.48 FALSE −0.69 −0.46 CSGALNACT1 3.14E−01 1.30E−01 −56.46−1.005860494 −21.10234746 −1.19 FALSE −5.34 −3.85 HLA-H 2.38E−017.55E−01 −56.36 −26.8522345 −2.691659575 −0.99 FALSE −2.77 −0.44 CLEC2B9.68E−04 1.64E−01 −55.69 −3.93 −40.47 −1.87 FALSE −8.63 −4.19 ATP1B14.75E−01 9.93E−02 −55.53 3.790248535 −73.66520645 −0.74 FALSE −3.09−1.56 DAG1 2.86E−01 6.40E−01 −55.41 −3.15 −5.62 −1.66 FALSE −0.71 −0.52NFKBIA 5.25E−03 5.77E−01 −55.35 −7.315272323 −17.05872829 −1.05 FALSE−4.18 −0.54 SRPX 3.36E−01 2.51E−01 −55.12 −7.37 −3.79 −2.09 FALSE −0.80−1.57 CASP1 6.92E−02 8.19E−01 −55.00 −1.031280571 −66.1978381 −0.96FALSE −1.32 0.38 DPYSL2 1.23E−02 7.32E−01 −54.92 −1.056511462−99.22916498 −1.14 FALSE −0.77 0.34 S100A1 1.82E−01 1.09E−01 −54.68−14.25397572 −14.42420921 −0.63 FALSE −0.61 −0.78 FLJ43663 Inf Inf−54.67 −6.490292736 −10.57910257 −1.20 FALSE −4.45 −4.16 UPP1 1.21E−017.67E−01 −54.34 −7.451372117 −2.528276372 −1.13 FALSE 1.67 1.17 APOE3.46E−01 1.80E−01 −54.04 −4.357609216 −10.06500479 0.32 FALSE 0.31 1.11LPL 1.87E−01 1.61E−01 −54.00 −6.59 −51.84 −2.19 FALSE −0.45 −0.45 KLF43.63E−02 9.02E−01 −53.99 −0.31 −23.01 −2.34 FALSE −0.55 −0.36 SLC20A13.66E−01 3.38E−01 −53.68 0.47 −18.37 −2.06 FALSE −2.41 −1.61 LGALS3BP1.92E−01 8.74E−01 −53.62 −12.98 −5.64 −1.68 FALSE −0.61 0.43 LINC001164.39E−01 1.53E−01 −53.33 0.38 −29.09 −1.90 FALSE NA NA RPS4Y1 8.64E−029.11E−01 −53.11 −64.09755214 −3.82061851 −0.66 FALSE 1.34 1.23 SQRDL9.82E−02 8.26E−01 −52.38 −5.25 −38.28 −3.08 FALSE −3.94 −1.26 ITM2B2.72E−02 7.97E−01 −52.21 −10.23 −13.51 −1.63 FALSE −5.41 −2.59 TMX44.28E−01 2.58E−01 −52.20 −1.16 −13.17 −1.39 FALSE −2.83 −1.33 IL6ST1.01E−02 3.26E−01 −52.05 −2.89 −6.37 −1.61 FALSE −1.92 −0.83 BIRC31.72E−01 7.32E−01 −51.42 −7.23 −41.19 −4.32 FALSE −7.28 −2.50 ANXA24.45E−01 5.66E−01 −51.27 −12.18 −8.25 −2.15 FALSE 0.78 0.68 ZBTB202.19E−01 7.01E−01 −51.13 −1.09 −25.68 −1.43 FALSE −0.42 0.31 GRN9.79E−02 5.69E−01 −51.04 −3.33479904 10.58500961 −0.92 FALSE 0.51 0.95SERPINE1 2.26E−01 8.94E−02 −50.78 0.45 −45.37 −2.08 FALSE −1.78 −0.47MT1X 9.41E−02 7.92E−01 −50.16 −2.90 −20.02 −1.51 FALSE −1.45 −2.13FCGR2C 6.04E−04 3.75E−01 −50.04 −6.560207399 −28.23432948 −0.90 FALSE−5.71 −2.00 ACSL3 4.57E−01 3.37E−01 −49.94 −3.939970091 −4.775352737−0.49 FALSE −0.93 −1.32 IFI27 2.77E−01 4.25E−01 −49.91 −24.12491388−7.193933103 −1.12 FALSE −3.69 −1.74 AEBP1 7.23E−03 7.36E−01 −49.86−0.652064041 −9.791519511 −1.24 FALSE −0.31 0.33 TIPARP 6.67E−025.81E−01 −49.73 −1.699010303 −20.12848456 −1.30 FALSE −2.25 −1.20 VAMP87.89E−02 4.82E−01 −49.73 −5.340727074 −25.95153555 −0.78 FALSE −0.771.19 DST 3.55E−01 6.19E−01 −48.89 −2.44 −3.35 −1.59 FALSE 0.47 0.55IFI35 1.88E−01 7.91E−01 −48.67 −7.02 −6.98 −2.31 FALSE −3.05 −1.00 ITGB13.60E−01 2.39E−01 −48.52 −3.58 −9.62 −2.66 FALSE −1.85 −1.87 BCL68.45E−02 8.06E−01 −48.50 −4.89 −22.66 −3.13 FALSE −4.25 −1.89 ERBB31.90E−01 6.37E−01 −48.36 −9.73134426 −0.439078261 −0.73 FALSE 0.53 0.33ZMYM6NB 6.10E−01 1.14E−01 −47.89 −1.77 −21.83 −1.45 FALSE NA NA CLIC41.22E−01 3.15E−01 −47.81 −1.16 −17.42 −1.41 FALSE −4.55 −3.87 FOS4.13E−01 6.43E−01 −47.57 −6.386092681 −1.042139346 −0.73 FALSE −0.87−0.36 IGF1R 3.62E−01 4.41E−01 −47.19 −1.54 −23.75 −1.37 FALSE −0.36−0.58 PLEKHB1 2.57E−02 3.38E−01 −46.81 6.095867912 −42.43208655 −0.54FALSE −1.60 −1.68 GOLGB1 5.56E−01 4.63E−01 −46.38 −4.661054566−8.368284482 −1.24 FALSE −2.84 −2.24 PSAP 1.12E−01 7.72E−01 −45.94−17.73630528 0.396372679 −1.07 FALSE −0.67 0.69 RNF145 4.06E−02 6.25E−01−45.93 −4.00 −9.25 −2.29 FALSE −2.59 −1.12 CTSL1 5.49E−01 2.91E−01−45.88 −13.12164871 −18.65717439 −0.84 FALSE 0.67 0.86 SYNGR2 2.82E−037.59E−01 −45.78 −5.09168104 −10.48190321 −0.94 FALSE −1.25 0.81 HTATIP28.60E−01 1.13E−01 −45.69 −2.00 −19.85 −2.43 FALSE −1.31 −1.68 KLF62.33E−02 6.52E−01 −45.62 −18.85 1.21 −1.57 FALSE −0.45 −0.44 LOC541471Inf Inf −45.38 −2.644136674 −11.14964202 −1.23 FALSE 1.16 1.08 SAT12.24E−01 3.62E−01 −44.81 −30.71664031 −1.26808839 −0.55 FALSE −0.81 0.50FBXO32 1.79E−01 2.88E−01 −44.73 0.322762583 −6.161648524 −0.32 FALSE0.70 0.40 S100A10 5.31E−03 6.74E−01 −44.66 −22.95 0.42 −2.01 FALSE 0.380.48 ATF3 4.52E−01 3.92E−01 −44.63 1.63 −38.08 −2.50 FALSE −1.55 0.34SCARB2 8.77E−02 6.12E−01 −44.43 −2.576905156 −2.01525226 −1.16 FALSE−1.06 −0.77 GPNMB 1.82E−01 7.59E−01 −44.30 −27.37333779 −0.672867612−0.74 FALSE 0.44 0.73 FCRLA 7.03E−03 9.79E−01 −44.01 −10.35 −13.12 −1.87FALSE −0.40 0.40 CLU 6.77E−01 4.88E−01 −43.85 2.663183144 −40.06196504−0.63 FALSE −1.13 0.40 ADM 6.89E−01 6.95E−02 −43.84 −4.543551718−28.96929856 −0.76 FALSE 0.30 0.30 TF 5.05E−01 4.79E−01 −43.65 −8.72−51.07 −1.33 FALSE −1.03 −0.56 CAST 2.14E−02 8.85E−01 −43.40 −2.23 −9.80−1.38 FALSE −1.51 −1.09 C10orf54 2.37E−01 5.08E−01 −43.23 −1.005464269−47.29182888 −1.01 FALSE −3.61 0.82 ITGA6 4.78E−01 4.15E−01 −43.18 −3.18−12.52 −2.60 FALSE −3.43 −1.94 PSMB9 1.02E−01 7.61E−01 −43.08 −9.75−11.68 −1.87 FALSE −6.40 −1.66 BACE2 3.23E−01 6.02E−01 −43.02−1.544458411 −3.818105651 −0.83 FALSE 2.35 1.69 GADD45B 4.04E−011.97E−01 −42.59 −1.28 −35.87 −1.53 FALSE −1.56 0.60 IFI27L2 4.94E−011.42E−01 −42.51 −11.43 −4.73 −1.31 FALSE −0.54 −0.68 FADS3 3.81E−015.31E−01 −42.38 −2.307281418 −10.48779629 −0.84 FALSE −0.73 −0.64 GPR1554.45E−01 3.44E−01 −42.36 −1.727392739 −9.730760161 −0.67 FALSE −2.99−1.44 IFNGR2 2.69E−02 5.64E−01 −42.34 −2.678730729 −5.824501595 −1.16FALSE −2.71 −1.79 NEAT1 1.24E−03 9.34E−01 −42.32 −3.957711442−4.816575504 −0.65 FALSE −2.07 −2.38 ARL6IP5 9.95E−02 8.05E−01 −42.03−5.061500123 −6.026877076 −1.20 FALSE −4.08 −2.21 GJB1 6.66E−02 6.60E−01−42.02 −3.94868444 −3.639865415 −0.32 FALSE −0.31 0.38 ACSL4 6.24E−013.88E−01 −41.97 −1.75 −14.86 −2.59 FALSE −6.05 −3.62 ATP1B3 2.92E−026.43E−01 −41.66 −2.82 −21.79 −3.00 FALSE −0.52 −0.79 ECM1 1.35E−015.94E−01 −41.65 −2.62 −6.36 −1.47 FALSE 2.45 1.30 APLP2 4.94E−011.91E−01 −41.49 1.55753898 −32.79252137 −0.99 FALSE −1.94 −2.11 ANGPTL4 4.14E−O1 2.31E−01 −41.48 −0.66 −57.88 −1.62 FALSE −0.39 0.38 GPR567.32E−03 6.52E−01 −41.45 −18.86181027 6.113225588 0.32 FALSE 1.27 1.10SYPL1 2.59E−01 7.74E−01 −41.38 −2.204809487 −11.39626417 −1.08 FALSE−1.57 −3.14 FNDC3B 2.11E−01 3.32E−01 −41.27 −1.78 −19.28 −1.86 FALSE−4.21 −2.77 CYBRD1 3.43E−01 9.88E−02 −41.01 −3.84 −6.47 −1.37 FALSE−1.60 −1.51 CTSA 1.55E−01 5.79E−01 −40.89 −3.17 −15.31 −1.86 FALSE 0.490.68 MCL1 4.84E−01 4.49E−01 −40.82 −0.665221316 −21.44924463 −1.22 FALSE−4.37 −1.75 LEF1 1.84E−01 7.45E−01 −40.69 −0.409062265 −22.95759126−0.36 FALSE −0.88 −0.46 BBX 2.43E−01 5.39E−01 −40.61 −0.61 −21.43 −1.67FALSE −3.83 −2.98 FKBP5 5.46E−01 2.10E−01 −40.55 −9.020160799−25.90285892 −1.24 FALSE −1.88 −0.53 FAM114A1 7.76E−01 2.18E−01 −40.47−3.16 −18.85 −1.90 FALSE −0.54 −0.53 LTBP3 1.66E−01 4.22E−01 −40.273.20667713 −16.87434626 −0.99 FALSE −2.15 −2.16 HSPA1A 9.37E−01 1.13E−01−40.16 9.037838299 −41.23649886 −0.31 FALSE 1.23 1.55 EPHX2 1.42E−012.31E−01 −40.08 −1.222892988 −48.82245871 −1.07 FALSE 0.77 0.95 ITGA76.60E−01 3.30E−01 −40.03 15.86332961 −39.83592494 0.37 FALSE 0.61 −0.61AGA 2.23E−01 7.32E−01 −39.91 0.536274519 −9.136581859 −0.68 FALSE −1.38−1.08 LYRM9 7.78E−03 8.61E−01 −39.80 1.964812062 −24.63102517 −0.69FALSE NA NA CREG1 2.30E−01 6.57E−01 −39.71 −1.46 −13.57 −1.52 FALSE−3.57 −3.22 IFI6 4.46E−01 3.38E−01 −39.64 −15.10 0.69 −1.73 FALSE −1.61−0.66 JUNB 3.17E−01 3.06E−01 −39.64 1.25 −18.40 −1.74 FALSE −1.43 −0.32SPTBN1 6.95E−03 6.68E−02 −39.48 −10.19774666 1.9742265 −0.96 FALSE 0.37−0.44 PRNP 1.27E−01 7.68E−01 −39.20 −0.86 −16.57 −1.55 FALSE −2.85 −2.80TNFSF4 9.86E−01 3.54E−03 −38.88 16.30016407 −42.88092111 −0.42 FALSE−3.81 −2.50 C8orf40 2.61E−01 2.10E−01 −38.60 −0.90 −18.77 −2.97 FALSE−0.99 −1.72 SEL1L 4.31E−01 4.74E−01 −38.58 −3.90 −1.91 −1.65 FALSE −3.19−2.36 SNX9 8.39E−02 9.43E−01 −38.40 4.21773408 −17.87314667 −0.31 FALSE−1.99 −2.10 KRT10 7.68E−01 1.86E−01 −38.33 6.615582665 −27.12457887−1.01 FALSE 2.72 0.66 EPDR1 1.94E−01 1.86E−01 −37.93 4.58 −42.43 −1.53FALSE −0.35 −0.76 EGR2 2.42E−01 1.28E−01 −37.72 −0.332043956−14.27003379 −1.16 FALSE −2.88 −0.63 GATSL3 1.63E−01 1.34E−02 −37.617.800143337 −24.63977081 −0.35 FALSE −0.94 −0.81 COL16A1 3.73E−013.56E−01 −37.54 −2.47 −38.81 −1.42 FALSE −1.72 −0.87 CD55 5.71E−011.64E−01 −37.49 −1.97067768 −9.604169548 −0.97 FALSE −1.46 −4.40 CRELD15.86E−01 6.12E−01 −37.35 5.47 −34.69 −1.57 FALSE −1.13 −0.68 SVIP5.84E−01 1.64E−01 −37.25 −0.974568455 −25.31871464 −0.74 FALSE −1.56−1.15 NFE2L1 8.30E−02 8.74E−01 −37.12 −0.70 −10.29 −1.42 FALSE 1.23 0.70PRDX1 6.51E−01 4.55E−01 −36.94 −3.087427147 −24.82199094 −0.54 FALSE0.70 0.62 B2M 4.43E−02 7.71E−01 −36.89 −21.10505197 −26.41773682 −1.10FALSE −7.29 −2.96 PDE4DIP 5.31E−01 5.09E−02 −36.89 −0.402602515−9.208907424 −0.71 FALSE 0.49 0.41 APOL1 1.22E−01 6.68E−01 −36.88−1.287252758 −17.14787261 −0.74 FALSE −5.74 −1.36 CREB3L2 4.17E−014.63E−01 −36.84 0.358284954 −2.018391143 −0.49 FALSE −0.60 −0.70 EVA1A1.57E−01 6.76E−01 −36.71 −0.761040106 −18.07156414 −0.66 FALSE NA NATIMP2 1.13E−01 8.26E−01 −36.71 −0.626230588 −4.251996112 −0.64 FALSE1.22 0.72 STAT3 1.90E−01 6.70E−01 −36.62 −0.369941565 −8.5293768 −1.07FALSE −3.12 −0.90 EZH1 5.10E−02 3.93E−01 −36.54 −0.417829156 −9.6734554−1.13 FALSE −2.33 −2.81 SPRY2 1.85E−02 2.34E−01 −36.26 −1.08 −25.28−1.61 FALSE −2.42 −2.39 ITGA10 6.22E−01 1.93E−01 −36.13 −2.05617709−7.093599209 −0.71 FALSE −1.57 −1.75 TGOLN2 2.71E−01 6.43E−01 −35.99−1.61 −8.04 −1.52 FALSE −2.20 −1.14 NFAT5 3.84E−02 6.11E−01 −35.92 −0.97−7.21 −1.45 FALSE −2.99 −2.31 CD46 7.87E−01 3.15E−01 −35.83 −11.65 −0.60−1.53 FALSE −4.30 −4.97 HLA-G 1.55E−01 5.82E−01 −35.67 −28.13806449−4.324585401 −1.15 FALSE −2.09 −0.41 NPC2 4.63E−01 2.90E−01 −35.66−14.30417724 0.495091905 −0.40 FALSE 0.54 1.18 LOC100127888 6.29E−028.12E−01 −35.63 −11.84 −3.62 −1.37 FALSE 0.83 −0.35 LXN 1.66E−015.62E−01 −35.60 −4.54 −40.35 −2.72 FALSE −0.74 −0.38 MT1M 3.49E−012.33E−01 −35.36 −14.10 −11.08 −3.18 FALSE −0.69 −0.68 C16orf45 3.02E−036.46E−01 −35.32 −0.76 −48.53 −1.56 FALSE −2.09 −1.93 LOXL3 8.91E−014.37E−02 −35.03 4.82 −40.56 −1.35 FALSE −3.28 −1.82 LINC00152 5.34E−013.79E−01 −34.97 −5.73 −8.24 −1.32 FALSE NA NA PDK4 6.52E−01 1.85E−01−34.90 −5.780192629 −25.27607059 −0.98 FALSE −0.86 −0.83 GEM 4.46E−011.12E−02 −34.88 1.13 −32.19 −1.60 FALSE −1.36 −0.81 CCDC47 2.14E−011.23E−01 −34.85 0.460464013 −4.080093569 −0.82 FALSE −0.76 −0.68 SAA12.70E−01 6.03E−01 −34.50 −14.25803074 −33.7718886 −1.07 FALSE −0.63−0.54 FAP 2.46E−01 1.13E−01 −34.42 4.359167404 −41.90455405 −0.39 FALSE−0.67 −0.60 IER3 9.45E−02 7.96E−01 −34.39 1.561694536 −19.81249058 −1.06FALSE −1.68 −2.71 LEPROT 6.81E−02 4.29E−01 −34.35 −3.84 −3.79 −1.37FALSE −1.36 −1.01 SQSTM1 3.08E−01 5.29E−01 −34.34 −8.65 −2.82 −1.56FALSE −0.46 −0.44 TMEM66 1.03E−01 1.17E−01 −34.23 −6.335487056−14.24789241 −1.11 FALSE −2.58 −1.09 BIN3 7.84E−02 8.47E−01 −34.16 −1.04−4.76 −1.88 FALSE 0.32 −0.68 H2AFJ 3.84E−02 5.04E−01 −34.07 −25.8724194216.36684482 −1.06 FALSE 2.18 1.36 TAPBPL 1.34E−01 7.56E−01 −33.96−0.367921789 −29.97601261 −1.09 FALSE −2.88 −0.43 CHPF 6.61E−01 4.31E−01−33.88 2.895612909 −13.08869319 −1.15 FALSE 0.71 −0.37 KIAA1551 3.03E−012.58E−01 −33.84 −2.17 −10.79 −2.28 FALSE NA NA CCPG1 6.35E−01 5.74E−01−33.73 −2.59 −3.53 −1.41 FALSE −3.84 −3.38 CHI3L1 4.46E−01 4.28E−01−33.64 0.396603931 −50.32339741 −1.07 FALSE −3.88 −1.18 TNFRSF10B2.48E−01 3.26E−01 −33.55 −1.29 −19.70 −1.58 FALSE 0.41 −0.32 ENDOD13.12E−01 7.77E−01 −33.51 −2.51853644 1.041232189 −1.04 FALSE −0.45 −1.09CLIP1 1.44E−02 6.78E−01 −33.48 −2.054976195 −6.358112418 −1.14 FALSE−0.55 −0.44 TMBIM1 8.50E−02 9.38E−01 −33.46 −7.42 −1.80 −1.93 FALSE 0.33−0.34 AHR 5.02E−02 6.01E−02 −33.45 −2.02 −16.52 −2.41 FALSE −3.48 −1.81TMED9 7.07E−01 4.41E−01 −33.43 3.860348819 −7.452321879 −0.87 FALSE 0.490.55 NPTN 1.61E−01 6.54E−01 −33.14 −0.52 −4.89 −1.37 FALSE −2.34 −2.31UBE2B 1.90E−01 3.56E−01 −33.08 −4.50 −9.49 −2.60 FALSE −3.96 −3.73 SYNE28.23E−02 8.84E−01 −33.05 −8.739833095 3.942337418 −0.55 FALSE −0.81 0.32MBNL1 8.92E−02 5.79E−01 −32.82 −5.43424163 0.46937185 −1.05 FALSE −5.55−2.89 FAM46A 3.69E−01 2.02E−02 −32.69 3.82 −22.62 −1.63 FALSE −1.06−1.10 IL12RB2 4.14E−01 3.95E−01 −32.68 −16.46671472 −5.376288421 −0.96FALSE −0.41 0.64 DDIT3 7.65E−01 3.64E−02 −32.63 1.447014332 −19.27750288−1.20 FALSE −0.70 −2.12 FOSB 1.43E−01 8.00E−01 −32.49 −0.796475507−4.31272261 −1.06 FALSE −1.11 0.34 CAV2 1.80E−01 2.59E−01 −32.43 −3.08−4.61 −1.36 FALSE −1.69 −1.13 STOM 3.22E−01 4.73E−01 −32.40 −4.411700006−0.601246398 −0.40 FALSE −0.52 1.09 SERINC1 6.29E−02 4.88E−01 −32.30−3.03 −11.26 −1.69 FALSE −1.80 −2.10 MT1F 5.29E−01 2.87E−01 −32.19−10.67 −7.72 −1.67 FALSE −0.35 0.31 FZD6 4.56E−02 4.75E−01 −32.14−4.466223946 −10.61084389 −0.42 FALSE −2.55 −3.38 G6PD 4.37E−02 7.80E−01−32.10 2.04 −13.40 −1.65 FALSE −0.35 0.40 MVP 4.11E−02 9.36E−01 −32.00−2.51 −3.07 −1.43 FALSE −1.53 −0.51 TMED10 3.04E−01 4.70E−01 −31.94−3.937577051 −1.491101847 −0.72 FALSE −0.78 −1.04 MCOLN3 5.15E−027.52E−01 −31.92 −1.592257374 −34.95981505 −1.28 FALSE 0.56 0.63 C4A5.68E−02 7.73E−01 −31.78 5.6383754 −63.95587021 −0.57 FALSE −3.86 −0.65CHPT1 1.14E−01 7.16E−01 −31.65 −1.71 −8.66 −1.96 FALSE −0.92 −0.97 TOB11.63E−01 2.88E−01 −31.63 −3.499775851 −9.257319016 −0.60 FALSE 0.32−0.67 ELK3 2.92E−01 4.28E−01 −31.32 0.690617385 −15.60885115 −0.75 FALSE−1.57 −1.23 RND3 3.53E−01 5.03E−01 −30.88 −4.70 −15.82 −2.44 FALSE −1.33−1.28 PHLDA1 1.23E−01 6.12E−01 −30.88 −3.554078519 −12.08070285 −1.05FALSE −0.72 −1.27 TRIB1 2.16E−01 4.24E−01 −30.87 −4.102846583−7.012535992 −1.14 FALSE −1.07 −0.49 PLOD3 6.85E−01 3.92E−01 −30.70−4.529043691 −0.521299179 −1.19 FALSE 0.50 −0.36 DUSP1 2.31E−01 1.61E−01−30.66 0.662164296 −14.2747394 −0.77 FALSE −1.45 −0.31 LAMA4 3.36E−011.86E−01 −30.65 1.304437005 −13.71409326 −0.96 FALSE −2.08 −1.15 ALCAM1.39E−01 5.13E−01 −30.52 −0.324216688 −7.2330932 −1.26 FALSE −0.64 0.45PRKAR1A 6.16E−01 5.09E−01 −30.49 −2.995748369 −5.80777074 −0.36 FALSE−2.49 −1.59 CYSTM1 1.56E−01 6.62E−01 −30.37 −5.01 −1.52 −1.52 FALSE NANA MPZ 7.95E−01 3.44E−02 −30.22 3.827991239 −17.8589262 −0.79 FALSE−0.98 −0.46 REEP5 4.44E−01 2.83E−01 −30.12 −5.08 −6.71 −2.22 FALSE −0.94−0.57 BCAP29 6.01E−02 2.85E−01 −30.07 −0.788569825 −8.452538217 −0.66FALSE −3.69 −3.59 PLEC 3.00E−01 1.49E−01 −29.99 0.32314 −11.40496196−1.07 FALSE −0.70 −0.48 CBLB 4.62E−02 6.91E−01 −29.96 1.160755876−17.28521246 −0.37 FALSE −0.90 −0.37 CHI3L2 4.24E−01 3.28E−01 −29.83−4.908298993 −29.80387401 −1.50 FALSE −2.40 −0.30 GRAMD3 2.24E−021.27E−01 −29.69 −3.175491376 −32.24829385 −2.48 FALSE −1.28 −0.61 CAMP2.58E−01 2.56E−01 −29.67 −6.537864387 −32.95941798 −1.04 FALSE −1.50−0.43 CSRP1 6.53E−01 4.51E−01 −29.65 −3.645548095 −4.555036259 −1.17FALSE −1.00 −1.12 ARMCX3 5.62E−01 4.06E−02 −29.33 −6.284817238−0.591876729 −1.90 FALSE −0.38 −0.36 CANX 2.70E−01 5.34E−01 −29.31−1.780081404 −6.181232682 −0.92 FALSE −0.75 −0.53 TXNIP 1.58E−018.52E−01 −29.27 −0.527598171 −4.214704474 0.37 FALSE −0.70 1.02 S100A164.52E−01 6.89E−01 −29.26 0.688460885 −13.99697 −0.84 FALSE −0.44 −1.03HEXB 3.66E−01 1.28E−01 −29.23 −6.435524884 −0.371050963 −1.21 FALSE−1.30 −0.79 WEE1 2.40E−02 3.83E−01 −29.22 −1.837664314 −10.45776818−0.94 FALSE −2.16 −1.20 CTSO 2.03E−01 3.03E−01 −29.18 −0.52913538−10.25117093 −0.89 FALSE −3.34 −0.64 PLOD2 2.29E−02 2.76E−01 −29.00−1.038914654 −11.95269747 −0.82 FALSE −1.68 −0.99 DAAM2 2.86E−018.20E−01 −28.93 0.995149536 −16.35413947 −0.37 FALSE 0.33 0.31 IQGAP12.26E−01 8.38E−01 −28.81 −5.892500327 3.999796242 −1.01 FALSE −1.12−0.63 ATP6V1B2 3.12E−02 9.33E−01 −28.81 −1.236034151 −5.74856853 −1.57FALSE −0.42 −0.37 PSMB8 8.52E−02 7.24E−01 −28.67 −5.117567066−8.854518735 −1.76 FALSE −4.67 −1.59 TES 1.42E−01 5.00E−01 −28.64−0.478011716 −32.21555921 −0.44 FALSE −0.88 0.34 ABHD2 1.04E−01 6.28E−01−28.54 −1.251595254 −10.61895132 −2.78 FALSE −1.09 −0.84 AKAP9 6.09E−011.96E−01 −28.52 0.35918391 −5.461432538 −0.42 FALSE −2.69 −2.68 LIF6.70E−01 3.44E−01 −28.52 3.073773887 −28.07039375 −1.10 FALSE −4.32−3.08 PLK3 1.38E−01 8.45E−01 −28.49 1.464108474 −11.44618117 −0.99 FALSE−0.50 −0.36 OSBPL5 6.95E−01 3.31E−02 −28.46 −2.269849969 −8.980489184−1.32 FALSE −0.98 −1.54 ADIPOR2 1.68E−01 8.80E−01 −28.35 −0.839832233−5.706827917 −1.02 FALSE 1.68 0.89 S100A4 7.37E−01 6.12E−02 −28.27−3.380134016 −64.01199908 −1.03 FALSE −1.02 −0.40 RTKN 6.89E−01 3.62E−01−28.22 −0.492129374 −9.892228036 −0.92 FALSE 0.53 −0.68 NR4A2 4.92E−026.31E−01 −28.21 −3.780760282 −0.423389847 −1.30 FALSE −2.24 −1.22PPAPDC1B 2.33E−01 9.19E−02 −28.10 −1.533802908 −10.81399535 −1.87 FALSE−1.98 −1.79 MAGEC2 6.50E−01 7.68E−02 −28.07 −2.1071117 −41.34703335−1.46 FALSE −1.05 −1.02 PDE4B 6.64E−01 2.67E−01 −28.03 −2.364902426−35.00944346 −2.37 FALSE −3.27 −0.50 AQP3 3.72E−01 7.16E−01 −28.03−9.058871123 −22.68044058 −1.01 FALSE 0.58 1.50 RTP4 1.96E−02 8.07E−01−27.94 −5.675337518 −6.019705486 −2.01 FALSE −2.18 −0.74 NIPAL3 7.84E−027.37E−01 −27.58 −2.025738972 −2.559743987 −0.76 FALSE −3.63 −3.63 PPP4R25.59E−01 3.37E−01 −27.53 −2.21758079 −3.375501383 −0.68 FALSE −2.28−1.72 NDRG1 3.17E−01 1.99E−01 −27.44 −2.248121009 −15.87689568 −0.56FALSE −3.88 −3.05 PFKP 3.56E−02 1.73E−01 −27.42 0.422114565 −4.660264324−0.49 FALSE 1.20 0.49 CD200 2.69E−01 5.64E−01 −27.40 −2.559052299−16.89013676 −2.02 FALSE −2.30 −0.77 SLC2A3 5.69E−01 3.59E−01 −27.38−1.861116025 −1.247312253 −0.79 FALSE 0.52 0.65 TRIM51 1.23E−02 9.86E−01−27.38 −22.00677143 −5.876405839 −0.74 FALSE NA NA TJP1 2.45E−012.61E−01 −27.23 0.387152694 −29.97857109 −0.85 FALSE −0.81 0.36 CPVL6.42E−01 1.96E−01 −27.04 0.662656107 −31.8573706 −0.59 FALSE −1.29 −0.44IFRD1 5.23E−02 1.74E−01 −27.01 4.489953381 −27.07247597 −0.51 FALSE−1.74 −3.39 LMNA 2.61E−01 7.35E−01 −26.99 −14.85556789 5.149222622 −0.90FALSE 0.57 0.39 TMEM30A 8.31E−02 1.20E−01 −26.95 1.296738123−9.650459473 −0.72 FALSE −4.05 −4.59 NAMPT 2.38E−01 8.88E−01 −26.92−0.599957745 −8.35441204 −1.27 FALSE −3.72 −2.18 INPP5F 1.79E−014.31E−01 −26.90 −4.825022902 0.536466924 −0.49 FALSE −2.07 −1.43DLGAP1-AS1 9.43E−01 5.69E−02 −26.86 −0.873888664 −5.983055739 −0.56FALSE NA NA ENTPD6 3.39E−01 2.81E−01 −26.81 0.918823136 −9.944809472−0.69 FALSE 0.64 0.62 ANKRD36BP1 3.05E−01 8.29E−01 −26.74 −0.387440246−1.450487926 −1.12 FALSE −0.84 0.33 DNASE2 3.82E−01 3.30E−01 −26.66−5.79271027 −3.087603241 −1.13 FALSE −0.85 −0.45 PARP9 1.77E−02 9.10E−01−26.62 −8.593426968 −3.79735601 −3.07 FALSE −6.05 −2.17 ETV4 4.66E−014.90E−01 −26.48 2.154806287 −40.7905761 −0.37 FALSE −0.31 −0.77 AKR1C31.43E−01 4.41E−01 −26.25 −4.534550779 −31.83008396 −1.29 FALSE −0.420.41 PIGT 8.38E−01 1.37E−01 −26.24 0.796637157 −21.82796134 −1.98 FALSE1.11 0.71 ANKRD28 6.58E−02 8.47E−01 −26.18 −0.835178911 −1.880605312−1.05 FALSE −0.34 −0.72 TCN1 2.30E−01 3.90E−01 −25.97 13.20783241−24.95604114 −0.76 FALSE 0.85 0.51 SERINC5 2.86E−01 3.40E−01 −25.90−1.367509487 −2.310450523 −0.56 FALSE −0.35 0.98 SLC38A2 2.45E−016.32E−01 −25.84 5.887885708 −14.851784 −0.51 FALSE −3.06 −2.13 SLC16A34.77E−01 5.69E−03 −25.80 −1.860338009 −2.425802885 −0.48 FALSE −0.530.41 ENO2 7.06E−02 3.19E−01 −25.77 −5.890890828 −0.712932382 −0.60 FALSE0.64 −0.60 ADAM9 2.70E−02 5.60E−01 −25.74 0.496294512 −4.870672147 −1.45FALSE −0.75 −0.46 P4HA2 2.45E−01 1.78E−01 −25.73 1.590533138−10.68944038 −1.54 FALSE 0.67 0.58 TRIM47 7.98E−02 9.46E−01 −25.63−1.850462382 −9.018178263 −0.70 FALSE −0.52 0.36 S100A13 1.28E−018.69E−01 −25.59 −0.978590241 −3.665918361 −0.34 FALSE 0.38 −0.44 SUMF23.63E−01 4.64E−01 −25.55 1.576955308 −9.135832478 −1.47 FALSE 0.43 −0.50LONP2 7.24E−02 6.03E−01 −25.52 −1.149798332 −2.114676254 −0.35 FALSE−0.92 −0.99 PJA2 1.03E−01 1.27E−02 −25.34 0.664063647 −8.490295655 −1.46FALSE −4.51 −2.82 NOTCH2 6.53E−02 9.23E−01 −25.27 1.062830302−18.14375992 −1.39 FALSE 1.89 1.93 FLNA 1.85E−01 6.62E−01 −25.251.245245646 −6.641620967 −0.69 FALSE 1.17 1.24 ETV5 1.03E−01 7.28E−01−25.16 −2.425088433 −2.095041157 −0.33 FALSE 0.56 0.31 IRF4 1.43E−011.55E−01 −25.14 −8.149769268 −1.664389215 −0.51 FALSE 1.06 1.71 RNF2131.31E−01 8.83E−01 −25.03 −2.256625015 −0.442801921 −0.70 FALSE −5.53−0.81 ACTN1 8.63E−02 6.82E−01 −24.87 −2.392087133 −0.461493171 −0.63FALSE 0.67 0.34 MAP1B 1.41E−01 3.16E−01 −24.85 16.48663287 −91.90376064−0.75 FALSE −1.34 −0.72 SIL1 7.50E−01 8.78E−02 −24.81 −0.575261468−7.539668952 −2.88 FALSE −0.51 0.38 PNPLA2 1.42E−02 9.22E−01 −24.79−3.113307912 −6.394818612 −1.78 FALSE −1.19 −0.63 TSPYL2 6.78E−011.52E−01 −24.72 3.778385825 −9.370339528 −0.51 FALSE −0.61 0.31 SLC44A11.06E−01 6.28E−01 −24.69 −2.380864194 −0.349647359 −0.92 FALSE −0.51−0.39 PARP4 6.46E−02 8.01E−01 −24.68 −4.15933969 −4.597805687 −1.69FALSE −2.92 −1.43 THBD 3.74E−01 3.56E−01 −24.64 14.85253744 −8.817215143−0.72 FALSE 0.36 0.50 ATP6AP2 2.46E−01 2.24E−01 −24.56 −2.737231423−3.50521377 −1.49 FALSE −4.10 −3.54 SLCO4A1 1.17E−01 4.29E−01 −24.54−12.32577077 −2.491285662 −1.12 FALSE 0.53 0.32 QDPR 3.87E−01 3.01E−01−24.46 0.848626308 −1.281479991 −0.58 FALSE 0.93 −0.37 ACSL1 3.34E−017.20E−01 −24.44 −1.343355398 −2.394218878 −0.74 FALSE −0.52 −0.42 PHF171.88E−01 3.97E−01 −24.41 −2.536355562 −9.66696095 −0.67 FALSE −1.39−0.76 PKM 4.16E−01 3.42E−01 −24.35 2.779782869 −25.72454124 0.30 FALSENA NA SUMF1 4.47E−02 9.62E−01 −24.20 0.385589079 −18.61768978 −1.61FALSE −0.41 −0.67 DIP2C 1.36E−01 5.96E−01 −24.12 −0.89530856−0.456829866 −0.77 FALSE 0.40 −0.78 CCDC109B 1.35E−01 5.33E−01 −24.12−5.921564373 −20.89758313 −2.54 FALSE −2.60 −1.01 CLCN3 3.24E−026.68E−01 −24.11 −1.199961056 −0.424869674 −1.19 FALSE 0.36 −0.49 UBE2L61.07E−01 9.31E−01 −24.10 −32.4126104 −8.305731147 −1.48 FALSE −7.72−2.59 SNCA 5.44E−02 5.43E−01 −24.09 −4.640547132 4.114790574 −0.52 FALSE1.38 0.88 PCM1 8.07E−03 3.32E−01 −24.08 0.54026566 0.722188884 −0.72FALSE −2.49 −2.26 GPR137B 6.68E−02 6.10E−01 −24.07 −11.242428784.384258609 −0.67 FALSE 0.75 −0.31 XPO7 2.20E−01 6.54E−01 −24.041.12830525 −11.77469529 −2.28 FALSE 0.54 −0.32 ACTN4 2.89E−01 6.30E−01−23.85 1.040958249 −7.668186482 −1.69 FALSE −1.41 −2.22 SERINC3 5.28E−016.88E−02 −23.84 0.812247624 −7.880971405 −1.79 FALSE −2.22 −1.93 RCAN14.73E−01 4.75E−01 −23.82 −7.64834132 1.424446662 −1.57 FALSE −1.78 −1.20RHOB 1.81E−01 2.40E−01 −23.80 −1.511826677 −3.896080823 −1.25 FALSE 1.010.81 GNPTG 5.17E−01 5.84E−01 −23.63 −2.367199494 −5.629075389 −1.60FALSE 0.40 −0.35 SHC4 8.71E−02 8.28E−01 −23.63 0.761842584 −1.213412465−0.41 FALSE −0.92 −0.56 RGS2 5.33E−01 2.47E−01 −23.60 0.640530514−35.26948489 −0.86 FALSE −1.66 −0.77 LOC729013 Inf Inf −23.34−1.347015309 −7.739237399 −0.38 FALSE NA NA SPTAN1 2.79E−01 6.02E−01−23.28 −3.516248421 −0.933499052 −1.09 FALSE 1.33 1.92 ROPN1B 1.96E−015.51E−01 −23.25 −0.392244359 −3.757229083 −1.17 FALSE −0.37 0.31 CD975.02E−02 6.09E−01 −23.17 −1.82842499 −11.21440814 −2.60 FALSE −4.58−1.91 HIST1H2BD 1.36E−01 7.30E−02 −22.98 −1.015586013 −7.336021298 −0.59FALSE 0.44 0.52 RNH1 3.44E−01 5.51E−01 −22.98 −15.57474178 −1.489768126−2.73 FALSE −0.36 −0.33 LAMB2 1.53E−01 7.07E−01 −22.88 −5.2233273646.005511857 −0.66 FALSE 1.34 1.26 CFB 2.69E−01 6.21E−01 −22.75−3.336017193 −33.81936425 −1.72 FALSE −5.01 −1.30 APOC1 3.51E−012.53E−01 −22.72 −5.24845496 −10.93115237 0.56 FALSE −0.97 0.35 CTTN2.95E−02 8.62E−01 −22.68 −21.47838673 −14.37231691 −0.93 TRUE 1.79 1.06SERPINI1 1.94E−01 3.95E−01 −22.64 7.367752032 −20.7152515 −0.82 FALSE−2.81 −2.79 AQP1 3.61E−01 1.34E−02 −22.54 −3.04043337 −24.54476544 −1.11FALSE −0.63 −0.40 C9orf89 1.20E−02 3.49E−01 −22.49 −0.827086987−7.744806672 −2.09 FALSE −1.25 −1.19 IGSF8 6.93E−01 1.66E−02 −22.42−3.56958779 3.520590252 −0.34 FALSE 1.85 0.62 LOXL4 3.71E−01 2.10E−01−22.33 1.334897324 −7.476659986 0.30 FALSE −2.35 −1.86 PARP14 5.18E−029.01E−01 −22.19 −8.097178503 −0.328886759 −1.20 FALSE −7.56 −1.95METTL7B 8.30E−01 3.07E−01 −22.13 5.765117863 −28.52875728 −0.53 FALSE−2.27 −1.03 DDIT4 1.02E−01 3.25E−01 −22.11 −5.255322514 −6.52630495−0.30 FALSE −0.73 0.42 ATP6AP1 6.86E−01 2.45E−01 −22.08 −2.478485511−4.57401432 −1.25 FALSE 2.24 1.59 EFCAB14 1.39E−01 7.38E−01 −22.08−3.316119059 −1.515729612 −0.55 FALSE NA NA HIPK3 9.45E−02 3.66E−01−22.07 −2.821393944 −4.287750809 −2.31 FALSE −2.16 −1.20 TRAM1 1.70E−013.45E−01 −22.00 −1.343023324 −5.818549856 −0.80 FALSE −2.96 −0.93 GNG121.51E−01 4.34E−01 −21.98 −3.285765565 −0.610412559 −1.15 FALSE −0.52−1.19 HEXIM1 3.90E−01 6.89E−01 −21.98 0.494765627 −1.209847864 −0.53FALSE 0.77 0.59 ARPC1B 4.19E−01 5.02E−01 −21.95 −5.014848929−0.378721015 −0.51 FALSE −0.44 0.43 TBC1D10A 7.46E−02 1.14E−01 −21.92−0.552709866 −1.475904674 −0.40 FALSE −0.67 −0.35 CELF2 1.04E−039.09E−01 −21.91 −11.30510408 4.398026966 −0.65 FALSE 0.72 1.32 AASS2.27E−02 4.80E−01 −21.87 −2.59954819 −6.596863423 −0.58 FALSE −1.59−1.62 BTG1 3.04E−02 8.04E−01 −21.84 −2.240405471 −2.125110401 0.32 FALSE−2.35 −2.87 ITGB5 2.13E−01 3.37E−01 −21.80 1.055482689 −7.664663167−1.23 FALSE −0.39 −0.59 LRP10 2.33E−03 9.26E−01 −21.76 −2.643825963−0.575237992 −0.86 FALSE 0.59 0.83 APOBEC3G 3.50E−01 1.02E−01 −21.76−1.435926819 −18.55196462 −1.19 FALSE −7.99 −3.16 NBR1 1.91E−01 1.24E−01−21.73 −0.401682359 −7.030124647 −1.45 FALSE −2.31 −1.99 ARHGAP186.67E−02 5.56E−01 −21.70 1.549818958 −6.08307845 −0.91 FALSE −3.47 −1.51RHBDF1 5.39E−01 3.26E−01 −21.64 −1.584125889 −5.041016329 −2.17 FALSE0.56 −0.47 C2orf82 8.24E−01 1.09E−01 −21.54 −3.589476747 −36.07674841−0.80 FALSE −0.76 −1.03 MRPS6 8.46E−01 6.04E−02 −21.54 3.8198103−31.19537764 −0.48 FALSE −3.76 −2.64 MFSD12 7.75E−02 7.20E−01 −21.46−10.32875944 0.847324385 −0.81 FALSE NA NA IL17RC 3.20E−03 9.68E−01−21.46 −0.950909415 −6.903765831 −1.33 FALSE 0.41 1.10 ORMDL3 2.42E−014.01E−01 −21.35 1.028831949 −12.12181027 −0.82 FALSE −1.56 −0.43 ERAP18.92E−03 8.00E−01 −21.33 −2.020343036 −1.07433683 −1.32 FALSE −4.25−0.52 DHRS3 3.85E−01 1.49E−01 −21.32 −3.674737662 −36.77125122 −0.89FALSE −3.81 −1.01 SMIM3 4.40E−01 1.74E−01 −21.31 −0.533091389−27.3010335 −1.51 FALSE NA NA MTRNR2L7 9.55E−01 4.31E−01 −21.30−0.641838996 −0.838254683 −0.36 FALSE NA NA MAN2B2 8.41E−02 6.73E−01−21.30 −3.188196571 −7.235940374 −2.17 FALSE −0.67 0.35 UBA7 9.53E−039.62E−01 −21.16 −6.237460628 −12.06515012 −2.74 FALSE −7.29 −1.47LOC100126784 2.74E−01 6.97E−01 −21.12 0.617459169 1.538338712 −0.33FALSE 0.91 0.40 ZMYND8 6.52E−01 4.23E−01 −21.09 8.46997889 −41.0598472−0.84 FALSE −0.71 −0.77 SERPINB1 7.69E−02 7.68E−01 −21.08 −7.082913363−3.513176367 −1.08 FALSE −2.14 −0.81 TUG1 7.67E−01 2.24E−01 −21.084.463316224 −0.484401657 −0.88 FALSE −0.47 −0.64 TMEM123 4.50E−014.45E−01 −21.02 1.608148266 −24.28280986 −1.17 FALSE −3.65 −3.37 OPTN1.75E−02 9.00E−01 −21.01 −15.25331624 6.957858787 −1.28 FALSE −1.82−0.67 SPP1 1.58E−01 2.37E−01 −20.95 29.30414836 −15.67592791 −0.31 FALSE−1.62 −0.80 VAMP5 2.01E−01 2.49E−01 −20.80 −18.92620281 −2.119672202−2.52 TRUE −4.70 −0.83 PFN1P2 2.26E−01 5.20E−01 −20.78 −4.2519559220.435712066 −1.31 FALSE NA NA STRIP2 2.90E−01 6.75E−01 −20.690.450218251 −16.83533974 0.68 FALSE NA NA TERF2IP 4.19E−01 4.95E−01−20.68 −0.523959722 −4.99899526 −1.15 FALSE −2.47 −2.74 CALD1 4.76E−024.98E−01 −20.63 −0.95351804 −3.241925514 −0.49 FALSE −1.72 −1.22 SDC41.32E−01 5.67E−02 −20.63 −1.191859966 −2.500483993 −0.76 FALSE −1.75−1.20 ST3GAL6 2.60E−02 4.09E−01 −20.62 −3.940416547 1.011466756 −0.39FALSE −1.54 −1.76 GABARAPL1 8.78E−02 5.78E−01 −20.60 0.899609729−10.63072995 −1.03 FALSE −1.21 −1.70 ATP2B4 3.11E−01 3.74E−01 −20.51−4.945501045 −0.713728198 −0.82 FALSE 0.42 −0.73 TYR 1.62E−01 8.43E−01−20.44 −5.806227943 8.573828698 0.35 FALSE 0.95 0.74 LPXN 9.73E−025.50E−01 −20.32 −4.724249565 −6.69091907 −2.49 FALSE −2.90 −0.99 NT5DC33.85E−01 7.50E−01 −20.30 3.824113566 −9.439658069 0.87 FALSE 1.45 1.08TMEM43 2.13E−01 7.61E−01 −20.29 −0.777872969 −10.65895763 −1.87 FALSE−0.43 −0.78 PPFIBP1 4.24E−01 4.93E−01 −20.24 1.128627461 −0.721079442−0.79 FALSE −1.32 −2.12 HPS5 1.63E−01 5.31E−01 −20.20 −4.91161177−0.941788497 −0.87 FALSE −1.99 −1.23 ST6GALNAC2 1.94E−01 4.17E−01 −20.18−15.32664647 2.850160806 −0.52 FALSE 0.64 0.45 GANAB 4.65E−01 2.60E−01−20.18 6.760249926 −6.862001928 −0.43 FALSE 0.73 −0.34 UBE2Z 1.30E−017.08E−01 −20.12 0.635275033 −4.157138659 −0.93 FALSE −0.40 0.34 BHLHE402.74E−01 3.89E−01 −20.08 −15.75869206 0.460982512 −1.07 FALSE 0.49 0.41ICAM1 1.40E−01 1.30E−01 −20.07 −5.42980278 −4.227678526 −0.90 FALSE−2.94 −0.81 MT1G 2.64E−01 6.28E−01 −20.07 −6.619086183 −19.45360494−1.78 FALSE −1.25 −0.92 TNFRSF1A 1.73E−01 3.01E−01 −20.05 1.213887782−9.901384801 −2.19 FALSE −0.58 −0.31 CEACAM1 8.88E−02 2.21E−01 −20.04−7.679312791 −0.618868776 −0.70 FALSE 0.31 −0.35 ATP6V0E2 1.88E−024.01E−01 −20.03 1.928199495 −14.26365141 −0.52 FALSE 0.57 0.41 IER26.61E−01 4.96E−01 −20.02 4.109943558 −25.7474651 −0.51 FALSE −0.30 0.35PELI1 4.39E−01 3.28E−01 −20.00 1.189921924 −35.6465558 −2.97 FALSE −2.64−1.15 GLCE 1.85E−01 3.72E−01 −19.98 1.177969643 −8.825783231 −0.32 FALSE−1.80 −2.12 AFAP1L2 6.59E−01 4.14E−02 −19.97 −1.073567177 −0.570275269−1.36 FALSE −2.23 −1.19 SRPR 6.59E−01 3.13E−01 −19.93 −0.531970765−4.906202103 −2.01 FALSE −0.93 −1.11 PEG10 6.25E−02 5.12E−01 −19.799.864562142 −70.65883456 −0.36 FALSE −1.59 −1.00 CCND1 2.58E−01 5.24E−01−19.79 −44.94838696 9.144440051 −0.44 FALSE 0.93 0.81 PDLIM5 1.61E−018.65E−01 −19.73 −1.229814252 −4.441449396 −0.81 FALSE −1.49 −0.84PTTG1IP 4.37E−01 4.41E−01 −19.73 −5.840061211 31.81674616 −0.46 FALSE1.42 0.70 PIM3 1.43E−01 4.70E−01 −19.67 −2.05856412 −2.93170429 −0.43FALSE −1.29 −0.88 LOXL2 1.30E−01 5.07E−02 −19.66 −2.227721553−17.75782926 −1.59 FALSE 0.63 0.59 CASP4 4.33E−02 5.13E−01 −19.66−1.060183077 −8.339833791 −2.26 FALSE −2.39 −0.56 SLC39A6 2.57E−012.62E−01 −19.62 −7.554501206 2.808826234 −0.42 FALSE 1.49 0.36 MICA1.60E−02 3.12E−01 −19.54 −4.830115449 −3.599631309 −1.12 FALSE 1.47 1.02PTPRM 4.72E−01 5.15E−01 −19.50 0.81484529 −4.358551311 −0.92 FALSE 0.730.87 IGFBP3 7.45E−01 6.80E−03 −19.50 −1.314794414 −34.09760334 −1.44FALSE −1.60 −1.31 OCIAD2 6.65E−01 2.76E−01 −19.49 1.305114076−79.98250015 −0.31 FALSE −1.69 −0.93 ASAHI 4.70E−01 3.55E−01 −19.40−8.977291847 12.30969044 −0.54 FALSE 1.02 0.93 BAMBI 7.62E−02 4.89E−01−19.40 −7.127650082 0.371125258 −0.66 FALSE 0.37 −0.67 CHN1 4.39E−011.42E−02 −19.28 4.899749645 −63.05446674 −1.01 FALSE −2.08 −1.63 SORT12.69E−01 6.04E−01 −19.05 −0.346897384 4.214233311 0.30 FALSE 1.07 0.79SPARCL1 2.75E−01 7.92E−02 −19.00 −5.863651519 −7.671589784 −0.45 FALSE−0.51 0.84 TYMP 5.50E−02 7.40E−01 −18.99 −7.727093689 −2.343707718 −1.57FALSE −2.68 0.30 LYST 3.98E−01 5.38E−01 −18.94 −2.644630966 2.41002413−0.84 FALSE −0.74 0.57 PACSIN2 1.92E−01 4.29E−01 −18.93 −1.371299596−1.411697598 −0.54 FALSE −0.34 −0.40 GNS 6.32E−01 5.79E−01 −18.78−4.823051083 −3.286157821 −1.51 FALSE −0.32 0.38 CSTB 1.50E−01 8.41E−02−18.77 −10.13996322 12.45898834 −0.64 FALSE 3.01 2.46 PRR4 5.94E−013.79E−02 −18.75 2.79869096 −29.63571458 −1.07 FALSE −0.94 −1.61 MFNG4.15E−01 6.44E−01 13.74 5.389614969 −7.877828514 0.76 FALSE −3.24 1.32RNMTL1 6.42E−01 2.90E−02 13.76 5.382060026 3.630827576 0.81 FALSE 1.850.97 6-Sep 3.42E−01 4.64E−01 13.79 4.196300143 5.140156942 1.00 FALSE−1.09 1.29 TUBGCP4 1.83E−02 8.31E−01 13.81 3.017098753 1.78840835 1.56FALSE 0.56 0.31 ARHGEF1 1.00E−01 4.17E−01 13.83 −0.583974655 21.570526331.53 FALSE −0.81 0.82 11-Sep 1.16E−01 1.74E−01 13.88 1.43039362924.22367679 0.89 FALSE 0.61 0.64 PCOLCE 2.45E−01 8.53E−02 13.9060.72202561 −6.40674755 1.57 TRUE −0.54 −0.79 SURF2 3.11E−01 8.17E−0313.90 3.289195508 4.660965337 0.89 FALSE 1.86 0.96 MRPL44 1.42E−012.49E−01 13.90 −0.452623362 9.001640945 0.59 FALSE 0.66 0.47 DCAF122.42E−01 2.44E−01 13.91 7.312065126 0.851451243 1.40 FALSE −0.37 0.47SAT2 5.10E−01 3.73E−01 13.92 12.75708283 5.628354728 1.21 FALSE 0.47−0.33 TSNAX 2.90E−01 5.50E−01 13.92 1.805752837 12.12793854 1.33 FALSE−1.86 −2.50 THOC3 8.77E−02 5.88E−01 13.92 4.098106348 1.957900047 0.65FALSE 1.18 0.59 PDCD5 7.35E−01 4.07E−01 13.98 6.751326589 6.836586916−0.35 FALSE 0.34 −1.77 MOCS3 3.15E−01 3.56E−02 14.00 1.4343702272.043955951 0.95 FALSE 0.34 −0.66 RBM4B 6.30E−01 1.64E−02 14.116.906518123 12.53811823 0.59 FALSE 0.40 0.38 MTX1 6.94E−01 2.54E−0114.12 5.91760368 2.667632146 1.24 FALSE 2.66 1.38 PRPF4 5.54E−011.41E−01 14.16 8.189088103 2.044969562 0.86 FALSE 2.38 1.76 HNRNPD5.99E−01 2.59E−01 14.17 4.315130309 7.503641237 1.01 FALSE −0.35 0.63MCM4 4.36E−01 2.25E−01 14.19 1.664350763 0.953479445 0.93 FALSE 1.501.57 AP3M1 8.55E−02 5.45E−01 14.24 0.629153205 6.41742361 1.11 FALSE0.51 0.84 XIST 7.44E−01 2.49E−02 14.30 29.59293181 7.697689322 0.45FALSE −1.93 −1.51 FAM64A 6.61E−01 8.41E−02 14.31 8.330570062−0.351042029 0.83 FALSE 1.24 0.88 G3BP1 4.02E−01 3.85E−01 14.3110.54566035 1.943806272 −0.40 FALSE −0.40 0.45 SNCG 4.74E−01 1.77E−0114.33 18.24763977 −7.528207908 0.97 FALSE 0.76 2.26 PI4KB 6.25E−012.16E−01 14.34 −0.797031323 29.52117947 0.41 FALSE 4.66 3.87 DDX465.72E−01 7.29E−02 14.35 5.88404805 5.552908424 0.75 FALSE −0.96 −0.52NNT 3.32E−01 5.47E−01 14.37 14.45967163 −6.905512186 1.70 FALSE 0.340.45 TIMM17A 8.05E−01 5.02E−02 14.40 4.529771377 5.274485432 0.98 FALSE0.67 −0.48 FTSJ3 7.42E−01 2.64E−02 14.41 11.3059408 2.197523397 0.77FALSE 1.50 1.16 HNRNPM 8.64E−01 8.82E−02 14.42 5.210267361 2.7380246140.91 FALSE 0.90 1.26 EXOSC6 3.95E−01 7.85E−01 14.43 0.4580978785.662862445 2.19 FALSE 1.75 0.76 IDH3B 8.23E−01 8.08E−02 14.433.288279147 0.694931133 0.49 TRUE 2.10 1.15 NHEJ1 6.12E−02 5.57E−0114.45 0.7467667 10.01366284 2.85 FALSE 3.41 2.34 COPS5 4.72E−01 2.19E−0114.49 13.43972244 −1.109684877 1.29 FALSE −3.27 −3.87 SBNO1 2.70E−016.76E−01 14.50 11.83280512 0.31530709 1.51 FALSE −0.35 0.40 TXNDC178.75E−01 2.11E−01 14.51 19.93469228 1.537234956 0.37 FALSE 0.51 −0.73HMG20A 4.15E−01 2.71E−01 14.51 8.768995629 3.138684411 1.23 FALSE −0.42−0.34 TRIB2 6.31E−01 4.40E−01 14.51 −1.325156749 36.78331778 1.14 FALSE0.56 0.37 CSK 1.54E−01 1.13E−01 14.53 1.783684971 3.908601844 0.83 FALSE0.84 3.57 B4GALT3 6.85E−01 3.24E−02 14.53 2.984723465 13.99293996 2.23FALSE 0.64 0.69 AIMP2 1.98E−01 2.26E−01 14.54 9.995109565 0.5892345570.68 FALSE 3.46 1.01 SUPT5H 7.15E−01 1.21E−01 14.56 0.52409465117.26213471 0.54 FALSE 2.66 1.82 POSTN 2.42E−01 9.71E−02 14.5725.61569592 −8.584718074 0.72 FALSE 0.46 0.73 GTF2H2C 6.04E−01 1.69E−0114.58 −0.403995243 5.926998115 2.48 FALSE −1.77 −1.09 GNL3 3.37E−014.02E−01 14.61 3.602849144 4.807218992 0.66 FALSE −1.34 −1.91 GBAS2.19E−01 2.58E−01 14.62 3.050038089 8.165505882 1.17 FALSE −1.66 −2.61MEST 4.42E−01 1.23E−01 14.64 26.73500059 −1.521354639 0.45 FALSE 0.430.43 CDH3 6.93E−02 4.14E−02 14.67 −4.060021324 27.37588719 0.38 FALSE3.88 3.59 PLEKHJ1 5.88E−01 1.16E−01 14.68 3.793428817 7.780824818 0.33FALSE 0.67 0.70 ECHS1 1.07E−01 2.20E−01 14.72 1.041998674 13.080422311.96 FALSE 2.27 1.81 SLC45A2 4.80E−01 3.24E−02 14.73 11.2315777320.34505987 1.51 FALSE 2.62 2.58 NEUROD1 5.30E−01 1.52E−01 14.7511.86664298 −10.86699078 0.69 FALSE −0.77 −1.69 ACTR1A 2.03E−01 2.17E−0214.76 0.616928184 16.24202821 0.49 FALSE 3.57 3.89 CD24 2.14E−012.06E−01 14.78 1.079125614 1.079391239 0.79 FALSE 0.64 1.79 LOC388796Inf Inf 14.79 −0.443428997 8.562706973 −0.46 FALSE 1.60 0.61 CDC205.51E−01 4.34E−02 14.80 4.913073148 0.753666063 0.63 FALSE 2.89 2.24TPI1 4.34E−01 1.30E−01 14.82 5.327916572 −0.744378475 0.77 TRUE 3.411.46 NOC2L 6.32E−01 2.28E−01 14.83 16.2653311 −1.958200998 1.14 TRUE1.46 0.80 CHCHD1 1.48E−01 5.42E−03 14.88 2.622835248 9.47306062 0.94FALSE 0.45 0.40 ALDH1B1 6.57E−01 3.31E−01 14.98 0.922296057 19.440161742.22 FALSE 0.94 0.33 NTHL1 3.95E−01 1.32E−01 15.01 10.157175582.446536902 1.34 FALSE 1.35 0.87 RARRES2 2.25E−01 5.11E−01 15.054.873224671 −0.301976127 0.91 FALSE −1.43 −0.37 SLC25A44 2.69E−011.85E−01 15.12 1.806177902 12.42707653 0.82 FALSE 2.81 2.10 ECD 3.16E−023.29E−01 15.16 0.508216518 14.92602402 1.10 FALSE −0.56 −0.81 ACBD64.72E−01 9.99E−02 15.18 4.54003142 6.492731101 0.49 FALSE 1.54 −0.33AURKA 4.90E−01 5.48E−03 15.18 4.926437071 1.29370898 1.38 FALSE 1.991.32 PRMT1 5.78E−01 3.22E−01 15.18 7.87390675 2.414514677 0.56 FALSE1.52 0.88 GNB2L1 3.13E−02 4.07E−01 15.22 0.754171752 3.35276588 0.35TRUE 0.40 −0.33 TOMM5 2.75E−01 5.50E−02 15.24 16.83196592 1.2218934991.00 FALSE 1.00 −0.31 SNRPF 2.17E−01 1.95E−01 15.27 15.001454793.094281947 0.67 FALSE 1.20 0.60 KLHL9 1.47E−01 7.18E−01 15.27−0.397375031 24.81289951 0.85 FALSE −1.85 −1.54 RNPS1 1.42E−01 4.75E−0115.29 2.320398903 3.782271567 1.76 FALSE 1.11 0.72 RPL36 5.72E−023.71E−01 15.33 2.178512724 26.41709158 −0.33 FALSE 0.50 0.33 SLC25A116.01E−01 2.68E−01 15.38 12.13755268 0.76179644 0.69 FALSE 1.65 1.15 FDPS3.64E−01 1.03E−01 15.41 3.097761019 7.762648036 0.62 FALSE 4.01 2.54PRPSAP2 4.16E−01 1.22E−01 15.41 9.218191038 2.298741055 1.45 FALSE −0.83−1.15 HAUS1 2.26E−01 3.37E−01 15.43 5.352399583 1.247369224 0.96 FALSE−1.42 −1.32 POLR2A 2.03E−01 8.81E−01 15.51 13.15051816 22.80329056 2.32FALSE 4.89 3.89 TDG 9.85E−01 3.25E−02 15.51 6.013013072 1.030694741 1.73FALSE −0.62 −0.69 EGLN2 1.62E−01 2.30E−01 15.51 4.254455956 6.3447070441.09 FALSE 1.99 2.30 CDCA5 5.88E−01 1.06E−01 15.53 5.285026282−0.307045502 0.82 FALSE 1.49 1.03 EIF2S2 6.74E−01 1.23E−02 15.558.293233204 −0.584561792 0.79 FALSE −1.33 −2.61 CACYBP 5.67E−01 5.28E−0215.56 2.448860208 6.784465091 1.43 FALSE −0.90 −1.21 TOMM22 5.29E−013.03E−03 15.57 11.70143787 1.10512845 1.41 FALSE 1.63 0.57 GLUL 4.19E−011.68E−01 15.60 −0.524584718 13.62207707 0.68 FALSE −1.32 0.33 KPNA22.93E−01 5.18E−03 15.60 4.991817798 6.639820973 0.58 FALSE 3.86 3.97GTF2E1 2.03E−01 5.56E−01 15.64 0.78347328 2.048719802 1.04 FALSE −1.63−1.64 LINC00665 1.28E−01 8.43E−01 15.74 1.516171688 5.780486589 1.43FALSE NA NA TARS2 4.95E−01 1.64E−01 15.74 3.244718053 7.167953196 1.02FALSE 2.01 1.13 ZSWIM7 4.97E−01 4.57E−01 15.77 5.395171027 2.0007490520.66 FALSE −0.62 −1.46 SPDYE5 2.06E−01 4.71E−01 15.80 0.7484772348.22009067 1.06 FALSE −0.85 −0.64 LSM4 6.34E−01 1.59E−01 15.824.47062328 2.251693195 0.39 FALSE 3.16 1.48 MYL9 4.21E−01 5.72E−02 15.880.696709556 7.750938059 0.63 FALSE 0.83 0.90 ATP5B 4.63E−01 7.46E−0215.89 2.737412219 3.557050178 1.66 FALSE 3.60 1.19 RGS3 2.55E−014.95E−01 15.91 6.172391972 3.484629082 −0.38 FALSE −0.57 0.57 CHTOP6.15E−01 8.77E−02 15.91 10.10615811 5.69056281 0.97 FALSE NA NA SMG75.02E−01 6.60E−03 15.93 5.209483431 11.99101659 2.02 FALSE 1.66 1.06EIF3J 2.68E−01 1.88E−01 16.00 14.28593134 −0.674223072 0.92 FALSE −2.31−3.82 MGC2752 Inf Inf 16.00 2.904335761 2.48840784 0.80 FALSE 1.05 0.69PAM 3.98E−01 5.38E−03 16.04 0.83707537 10.51755539 0.48 FALSE −0.65−0.55 GSTO1 6.07E−02 5.15E−01 16.05 −1.337030558 62.19279211 0.95 FALSE1.72 0.92 RABEP1 8.74E−01 1.42E−01 16.06 21.2928448 4.656282388 0.49FALSE −0.59 −0.83 KIF2C 7.82E−01 4.29E−02 16.11 6.859855363 1.8542704070.97 FALSE 2.25 1.77 CCNB2 2.81E−01 2.26E−01 16.12 3.9192302160.973041322 0.76 FALSE 1.37 0.69 NEK5 1.56E−01 8.32E−01 16.17−0.324543846 3.958302922 0.56 FALSE 0.47 −0.69 PPIF 3.27E−02 9.52E−0216.22 4.347882752 2.129355273 0.32 FALSE 3.33 3.00 C17orf49 8.03E−013.47E−01 16.22 9.736718533 0.87005317 0.67 FALSE −0.49 0.64 EXOSC55.33E−01 4.78E−01 16.26 4.490272348 1.542142828 0.38 FALSE 0.48 −0.45MAP1LC3C 4.65E−01 1.06E−01 16.27 −1.592062983 3.554313965 1.34 FALSE0.54 1.25 TUBB4A 9.06E−02 5.47E−01 16.29 −18.47518133 78.69139618 0.66TRUE NA NA EIF3G 2.66E−01 4.13E−01 16.30 0.485973534 14.91167008 −0.34FALSE 0.45 0.32 KIRREL 7.10E−01 1.91E−01 16.31 1.457831877 23.609609211.24 FALSE 1.50 2.01 ID3 4.40E−01 4.62E−02 16.33 6.385801262 6.6618763031.01 FALSE −0.70 0.37 CCNB1IP1 9.37E−02 6.45E−01 16.37 1.0836652568.087590455 0.98 FALSE 0.41 −0.93 IL6R 1.64E−01 1.16E−01 16.40−1.548267241 43.85250904 1.24 FALSE 0.72 2.10 RPS10 1.11E−01 1.50E−0116.42 3.683944948 16.30339108 0.76 FALSE 0.71 0.42 PKN1 5.51E−014.88E−01 16.42 13.74625835 3.306345432 0.70 FALSE −0.75 −0.56 C10orf327.21E−02 4.78E−01 16.43 −1.253078131 10.41824098 1.99 FALSE −1.13 −0.68SKA1 9.28E−02 2.26E−02 16.59 0.563847042 6.4942639 1.74 FALSE 1.34 1.20MRPS10 4.85E−01 8.68E−02 16.61 11.13816237 1.780643088 0.73 FALSE −0.56−1.54 CKB 7.19E−01 2.83E−01 16.62 0.94366682 −0.673071985 0.87 TRUE 0.910.69 CDCA8 6.65E−01 4.59E−02 16.62 5.935347842 3.409036488 0.85 FALSE3.42 2.98 ATP5A1 3.11E−02 3.82E−01 16.68 4.114811371 5.5164772 1.16FALSE 2.11 1.65 TTYH3 8.19E−01 6.55E−02 16.68 −0.839172467 25.99819560.60 FALSE 6.36 5.75 WDR6 2.40E−01 6.41E−01 16.69 2.8454458 11.146821252.13 FALSE 0.52 0.72 SLC5A6 6.91E−01 2.34E−01 16.79 12.877466050.747794957 0.73 FALSE 1.64 1.27 FAM213A 2.19E−01 5.17E−02 16.830.649223104 18.38276775 1.31 FALSE NA NA SNRPA1 9.48E−01 1.16E−01 16.888.418866258 1.511738171 1.59 FALSE 0.34 −0.32 MARCKSL1 6.64E−01 2.42E−0116.89 11.85693628 −0.322901855 0.35 FALSE 0.54 1.18 DDX39A 6.91E−011.20E−01 16.91 0.618867402 13.25993888 0.54 FALSE NA NA BEX1 6.18E−014.03E−02 16.92 15.14930944 −3.639527861 0.58 FALSE 0.98 0.40 ZNF5263.02E−01 5.04E−01 16.95 0.4369126 4.005769467 1.12 FALSE 1.81 0.94SMCR7L 2.03E−01 2.97E−01 17.02 7.881351856 5.194504892 2.41 FALSE 2.291.25 FAM126A 5.19E−01 3.75E−02 17.08 4.35274429 8.584972 0.63 FALSE−1.33 −1.06 LSM14A 5.40E−01 3.49E−01 17.11 0.425148121 20.86039965 0.55FALSE −1.33 −1.39 FDXR 5.12E−01 4.05E−01 17.12 1.67368801 16.383310441.47 FALSE 2.00 1.47 SLC19A1 5.55E−01 2.28E−01 17.15 5.58586663914.84646384 0.81 FALSE 2.13 2.18 GAGE12J 1.82E−01 2.36E−01 17.1617.96014408 −14.68096465 0.33 FALSE −1.39 −2.77 OCA2 1.40E−01 2.52E−0217.16 −5.354814935 28.5171977 1.11 FALSE 5.56 3.33 RBBP4 8.19E−011.59E−01 17.17 7.90217054 3.958262309 1.72 FALSE −0.54 0.47 NIP71.48E−01 4.29E−01 17.34 7.740805625 3.790249229 0.73 FALSE 0.63 −0.54PRPF31 6.36E−01 4.74E−01 17.37 3.746194298 5.153934765 0.79 FALSE 1.420.91 MKI67IP 4.95E−01 4.41E−01 17.46 5.342737904 3.950369618 0.74 FALSE−0.37 −1.42 TRUB2 7.07E−01 7.54E−02 17.48 4.61575142 4.893775965 2.04FALSE 2.77 1.68 METTL13 3.47E−01 4.85E−02 17.49 3.1052895 5.771837430.61 FALSE 2.17 1.65 HMGB1 3.33E−01 2.08E−01 17.50 6.1463151892.060104614 0.82 FALSE −1.52 −1.07 RCC1 6.07E−01 2.04E−01 17.526.917331724 3.981254561 0.87 FALSE 3.62 1.69 RPA1 3.28E−01 4.21E−0117.53 5.961074344 5.617977147 1.25 FALSE 1.36 1.23 HNRNPUL1 1.01E−012.70E−01 17.55 0.680339536 16.42646971 0.56 FALSE 3.18 3.24 NDUFV34.85E−01 2.61E−01 17.56 2.992981728 18.75771812 1.14 FALSE 2.18 2.14RQCD1 7.06E−01 1.40E−01 17.57 2.435033782 2.120791626 −0.41 FALSE 1.931.80 TCF4 3.80E−01 1.16E−01 17.62 9.236103162 −2.679784236 0.53 FALSE−1.45 −0.68 C20orf27 4.98E−01 4.60E−01 17.62 7.631118695 10.619495810.86 FALSE 3.11 2.07 CCT4 3.45E−01 9.10E−02 17.65 2.1704023396.399317541 2.01 FALSE 0.42 −0.48 VPS53 1.15E−01 3.30E−01 17.690.335449031 37.01970924 1.02 FALSE 5.99 4.71 WDR46 2.26E−01 4.31E−0117.76 8.702406207 1.725874114 1.39 FALSE 1.37 0.53 NEFL 7.01E−012.22E−02 17.76 11.97364126 −5.921165572 0.60 FALSE 1.26 0.51 TCEA38.40E−01 7.29E−02 17.83 0.459042162 4.038014366 1.40 FALSE 0.52 −0.40GAGE6 1.00E+00 1.00E+00 17.86 16.08931781 −14.75860399 0.66 FALSE NA NAGALT 1.33E−01 6.34E−01 17.87 1.976498011 20.98185221 2.04 FALSE −0.540.38 SNRNP40 8.89E−01 5.60E−02 17.90 8.554249159 4.948734856 0.85 FALSE0.87 1.24 CRK 8.58E−01 1.99E−01 17.94 3.670575611 7.55391113 0.91 FALSE0.87 0.76 GNL3L 5.40E−01 2.97E−01 17.96 4.387265453 23.90814734 1.25FALSE 2.02 2.37 NUF2 7.59E−01 7.74E−02 17.97 4.131933124 3.6476070351.05 FALSE −0.49 −0.70 SERPINB9 2.32E−01 9.95E−02 17.99 −2.380561982.914786989 1.08 FALSE −1.77 −0.41 ZFP36L1 1.21E−01 4.84E−01 18.015.938605734 19.42990388 1.51 FALSE −0.55 0.31 MRPS2 3.15E−02 1.72E−0118.02 4.542140417 7.779275401 2.47 FALSE 6.06 4.32 NENF 7.99E−012.86E−01 18.04 6.800958187 30.65598274 1.02 FALSE 1.98 0.56 DUSP128.17E−01 2.93E−01 18.14 3.468611254 6.132917887 1.25 FALSE −0.48 −1.03FLJ30403 7.61E−02 8.71E−01 18.15 −0.598011003 3.407988308 1.44 FALSE NANA APEX1 7.41E−02 1.70E−01 18.19 5.445008003 9.919076697 0.96 FALSE−0.31 −0.60 NUP62 5.48E−01 4.64E−01 18.22 2.387450184 4.82254016 1.22FALSE 1.90 3.41 LYPLA2 4.25E−01 4.36E−01 18.23 13.82195911 2.6159395261.18 FALSE 1.38 0.79 EEF1D 3.44E−01 6.12E−01 18.28 0.9857598937.451311433 1.02 FALSE −0.45 1.00 ABCF1 4.22E−01 1.35E−01 18.317.435248233 0.356070614 1.34 FALSE 3.23 2.37 SKAP2 2.81E−01 3.45E−0118.37 0.456404247 23.72086612 0.76 TRUE −5.45 −3.16 GPS2 6.67E−013.04E−01 18.40 4.308701037 7.881185647 0.55 FALSE 0.87 −0.37 SNRPA2.81E−01 1.16E−01 18.50 3.411530561 7.835454232 1.66 FALSE 1.41 1.64SNRPD1 5.32E−01 2.38E−02 18.60 21.15658975 −0.554113785 0.82 TRUE 0.74−0.30 NR2F6 5.66E−01 3.63E−01 18.64 8.495360144 6.727710363 1.64 FALSE3.63 2.14 IMPDH2 7.55E−02 4.30E−01 18.71 0.535373592 30.68574445 1.02FALSE 1.81 1.03 PSMC4 9.11E−01 1.04E−01 18.73 8.390998517 3.1140012910.46 FALSE 1.33 0.75 GPM6B 3.21E−01 7.52E−01 18.77 4.86231042833.47289854 0.32 FALSE −0.57 −1.24 SNRPE 7.55E−01 3.43E−02 18.8016.92686645 0.331937635 0.74 TRUE 1.03 −0.49 ASS1 4.62E−01 1.92E−0118.80 14.90230724 −0.463357928 0.85 FALSE 0.92 1.15 SF3B2 2.20E−016.96E−01 18.81 10.70469624 15.01569271 0.94 FALSE −0.31 −0.94 NDST13.03E−01 6.10E−02 18.82 7.522230908 12.72783941 0.34 FALSE 2.20 2.95RBM4 5.21E−01 4.82E−01 18.84 12.59107638 10.00071396 1.52 FALSE 0.550.52 SERPINH1 9.28E−01 8.95E−03 18.85 37.928133 8.200485704 0.49 FALSE0.99 0.91 RBP1 8.03E−01 2.16E−01 18.86 12.31936246 −4.522015287 −0.31FALSE 0.36 −0.37 SCO1 8.30E−01 2.85E−01 18.86 12.77799115 2.9744787370.72 FALSE 0.54 −0.48 RAB20 7.25E−01 1.55E−01 18.87 −0.53812476716.58525585 1.08 FALSE −0.59 1.11 CRABP2 4.46E−01 4.25E−02 18.885.991748766 0.818296256 0.73 FALSE 2.76 1.86 AURKB 5.17E−01 1.81E−0218.88 9.869762355 0.859165871 0.75 FALSE 2.28 1.41 DCTN5 1.03E−013.03E−01 18.90 3.150239057 6.140267676 1.32 FALSE 1.98 1.58 POLD14.07E−01 2.17E−01 18.90 2.322155697 4.865956872 0.55 FALSE 1.04 1.35ENY2 6.49E−01 3.68E−01 18.91 24.95069297 −0.410508403 1.77 FALSE −1.25−1.00 QARS 4.42E−02 3.13E−01 18.96 3.706877301 9.488292408 2.20 FALSE2.33 1.73 TOP1MT 7.94E−01 1.59E−01 19.00 2.138074483 7.061622399 1.01FALSE 0.46 −0.34 MPDU1 2.78E−01 1.02E−01 19.02 12.07276379 5.7586526931.46 FALSE 2.19 2.00 SMC3 1.43E−01 2.89E−01 19.04 2.20047574826.65516067 1.37 FALSE −1.26 −1.19 DTD2 7.61E−02 7.38E−01 19.060.454680038 11.47425732 1.86 FALSE NA NA TATDN1 1.17E−01 6.67E−01 19.106.785825964 2.252004297 1.28 FALSE −2.97 −3.70 UQCRC2 2.53E−02 3.72E−0119.12 7.938348231 5.090440135 0.94 FALSE 0.45 −0.44 RPP30 1.91E−012.11E−01 19.13 0.301420634 11.77733863 1.87 FALSE −0.60 −1.17 ATXN106.94E−01 2.97E−01 19.14 15.77144524 13.4473554 2.30 FALSE 0.57 −0.50WDR81 9.64E−02 8.16E−01 19.17 1.702392177 25.93904876 1.27 FALSE 2.802.13 PEPD 5.58E−01 2.73E−01 19.18 4.936443511 11.49578245 1.14 FALSE2.63 1.77 GAGE2B 2.57E−01 4.80E−01 19.18 17.70105474 −15.35736178 0.61FALSE −0.98 −2.19 FEN1 1.07E−01 2.62E−01 19.24 8.650445933 5.6538477130.63 FALSE 1.14 0.66 MRPS12 5.69E−01 1.66E−01 19.31 5.9301759035.311619169 1.32 FALSE 2.84 1.58 FKBP4 6.18E−01 5.52E−02 19.3610.29840259 1.108434516 1.06 FALSE 3.95 2.47 ALAS1 5.54E−01 3.19E−0219.38 5.938125987 9.878635076 1.06 FALSE 1.02 1.71 DPP9 1.83E−011.89E−01 19.42 −0.678639926 18.41244692 0.58 FALSE 2.05 1.88 ELAC25.97E−01 2.82E−01 19.45 12.02634776 3.287839364 0.85 FALSE 3.04 1.34RPS21 3.21E−01 5.10E−02 19.59 15.48074181 4.433652949 0.81 FALSE −0.61−0.82 HYPK 9.14E−02 8.26E−02 19.62 15.88253495 −0.541561047 0.94 TRUE NANA THEM4 3.55E−01 4.66E−01 19.63 2.641838036 14.1049993 1.04 FALSE 0.60−0.35 NXN 9.91E−01 1.25E−02 19.72 −0.570380212 4.96539098 0.91 FALSE0.84 1.70 ABR 3.48E−01 6.70E−01 19.73 1.706300196 19.36139174 0.77 FALSE2.16 1.33 DARS 3.52E−01 2.45E−01 19.76 5.49558121 7.54473926 1.48 FALSE−1.49 −3.22 KCNAB2 6.30E−02 6.76E−01 19.79 −3.601301043 82.174982 1.14FALSE 1.31 1.95 NUSAP1 1.31E−01 1.92E−01 19.90 4.885093685 2.5766919790.97 FALSE 0.59 0.37 STOML2 3.74E−01 6.99E−02 20.04 8.7700916252.098212208 1.02 FALSE 0.98 0.41 TOP2A 7.94E−01 4.05E−02 20.044.27101052 1.775930792 1.05 FALSE 0.51 0.43 INTS7 8.16E−01 4.00E−0220.23 6.6444416 3.720076088 0.79 FALSE 0.71 0.87 MFAP4 2.69E−01 1.24E−0120.27 17.64876071 −2.070632726 1.07 FALSE 0.53 2.01 MYADM 1.93E−016.15E−01 20.29 10.61246616 11.67475742 1.39 FALSE 0.56 0.81 POLR3C8.17E−01 1.79E−01 20.29 1.698797211 13.48631224 1.26 FALSE 2.24 0.39OXA1L 1.12E−02 3.08E−01 20.35 −0.321055708 42.38746933 2.14 FALSE 1.480.84 RRP15 6.36E−01 1.43E−01 20.36 4.607755076 2.111990874 0.77 FALSE−0.71 −2.44 GAS5 1.29E−01 5.14E−01 20.37 0.472735128 48.15462574 0.76FALSE 0.36 −0.61 HMGN1 6.43E−01 1.36E−01 20.39 6.749846284 4.0900253832.61 FALSE −1.19 −0.56 BIRC5 4.62E−01 2.77E−01 20.53 3.9889601472.589116396 0.84 FALSE 2.08 1.65 NEK2 8.14E−01 3.28E−02 20.555.416078429 2.176491052 1.71 FALSE 1.32 0.82 RRS1 8.28E−01 1.39E−0120.58 10.21643123 1.580673648 0.47 FALSE 0.67 0.47 PPP5C 4.71E−012.16E−01 20.62 1.771526742 8.368988743 0.76 FALSE 1.43 0.61 ARPC51.89E−01 8.71E−02 20.70 1.972299705 13.38339241 1.47 FALSE −3.00 −2.34TMEM206 3.87E−01 7.69E−02 20.75 8.747393842 9.677672637 2.21 FALSE 0.45−0.31 GAGE4 9.87E−02 9.02E−01 20.76 20.72965183 −14.00709129 0.42 FALSE−1.23 −2.28 EML4 9.25E−01 8.77E−02 20.79 12.05217543 6.934825832 1.60FALSE −0.36 0.76 NT5DC2 1.03E−01 3.47E−02 20.89 8.113534256 5.6623428790.87 FALSE 4.32 1.93 GAGE12H 6.82E−01 2.28E−01 20.99 18.51606224−13.97338677 0.70 FALSE NA NA PA2G4 1.68E−01 1.03E−01 21.05 5.497595071.679971385 1.66 FALSE 3.15 1.29 LOC100133445 5.36E−01 3.78E−01 21.09−3.452678468 36.55092064 0.98 FALSE NA NA RRM2 4.18E−01 5.40E−02 21.193.042468097 2.545029055 0.55 FALSE 0.92 0.82 GAGE2D 8.73E−03 3.59E−0121.20 20.08829393 −14.28059448 0.62 FALSE −1.16 −2.67 MRPL9 8.73E−014.07E−03 21.35 5.97577942 6.146484827 1.12 FALSE 2.40 1.13 TMEM114.08E−01 1.35E−01 21.40 20.12015326 1.018469789 0.89 FALSE 2.30 0.92TPM4 2.84E−01 6.94E−02 21.55 8.611761357 10.14109291 −0.31 FALSE −0.32−0.56 ESRG 1.86E−01 7.18E−01 21.56 −0.451092433 19.12852841 1.43 FALSENA NA SLC25A5 1.55E−01 6.97E−02 21.57 2.102500624 13.09751618 0.79 FALSE1.48 1.20 CYP51A1 2.25E−01 6.21E−01 21.57 −1.084958837 22.75159578 0.35FALSE 0.48 −0.53 TBXA2R 7.68E−01 7.92E−02 21.58 −1.441369813 22.298055711.63 FALSE −0.58 −0.43 LOC100128252 Inf Inf 21.59 25.17189358−14.09632693 0.36 FALSE NA NA SKA2 8.87E−01 9.62E−02 21.67 5.3165472779.937930469 1.05 FALSE −0.60 0.37 RUSC1 4.21E−02 3.03E−01 21.751.660172441 20.89322619 0.95 FALSE 2.59 1.08 PSTPIP2 5.69E−01 3.55E−0121.76 −1.750472311 11.72131361 1.49 FALSE −2.63 0.44 LMCD1 1.57E−019.29E−01 21.91 6.221082642 20.61172886 2.05 FALSE −0.68 −0.33 TIMM236.53E−03 8.93E−02 21.92 5.327664989 21.55010632 2.07 FALSE NA NA NARS25.28E−01 9.12E−02 21.93 7.661886481 16.67340475 1.86 FALSE 2.52 0.96STRAP 6.79E−01 3.63E−01 21.97 4.999612565 3.18913343 1.85 FALSE 2.080.65 XRCC5 7.17E−01 2.58E−01 22.00 10.2024523 3.783862242 2.03 FALSE0.45 −0.49 EEF1G 4.35E−03 6.07E−01 22.24 3.623195074 11.37785233 0.69FALSE 0.57 0.44 FLAD1 2.73E−01 9.01E−02 22.24 9.115959046 4.8899006611.14 FALSE 5.30 3.34 PRDX3 1.28E−01 7.54E−01 22.26 1.50688944432.24191804 1.77 FALSE −2.28 −1.43 GAGE2E 1.19E−01 2.55E−01 22.3620.18764216 −14.80560626 0.69 FALSE −1.21 −2.41 TUBGCP2 1.31E−015.85E−02 22.66 −0.633889067 43.15291198 0.99 FALSE 0.85 1.13 ORC67.21E−01 1.58E−01 22.71 0.700919811 7.219074042 1.94 FALSE NA NA GAGE12G4.76E−01 1.69E−01 22.73 21.55374302 −14.04755971 0.65 FALSE NA NA TSTD13.33E−02 9.76E−01 22.77 −4.022382197 28.17986342 0.77 FALSE −1.68 −0.68GAGE12E 8.00E−01 6.52E−01 22.80 22.0897866 −14.2987637 0.63 FALSE NA NAGAGE12C 6.13E−01 4.14E−01 22.81 22.08782445 −14.30284956 0.63 FALSE NANA NOP56 2.92E−01 1.32E−01 22.85 5.832178979 12.65094704 0.93 FALSE 1.580.68 HNRNPA1P10 4.99E−01 2.94E−01 22.87 10.61174151 6.927854056 1.18FALSE NA NA H3F3AP4 3.96E−01 6.95E−01 22.91 11.05089081 0.790953059 1.18FALSE NA NA ALDH18A1 3.46E−01 2.20E−02 22.94 15.61796755 10.128461670.91 FALSE 1.76 1.87 HN1 1.94E−01 5.05E−02 23.04 12.03860552 3.7755252970.96 FALSE 1.78 1.68 CPXM1 5.09E−01 3.38E−02 23.05 34.54741553−19.2357018 0.59 FALSE 2.24 1.64 SEMA6A 2.72E−01 2.53E−01 23.062.112698771 36.86586147 0.89 FALSE 5.02 3.59 PLTP 1.26E−01 1.21E−0123.23 0.705496057 32.7770504 −0.35 FALSE 0.93 1.16 NAPRT1 1.05E−017.78E−01 23.49 −2.243806067 26.63418143 1.37 TRUE 0.57 0.58 CPSF14.77E−01 5.25E−01 23.53 4.938813475 18.99445597 2.32 FALSE 0.49 1.64BUB3 5.98E−03 2.03E−01 23.57 3.787914349 14.66044954 0.94 FALSE −0.59−0.47 RGS16 7.20E−01 1.09E−01 23.66 24.77150312 1.061623763 0.59 FALSE−1.88 −0.96 AFMID 4.63E−01 6.89E−01 23.73 3.422504125 5.089000714 1.48FALSE 0.59 −0.43 SSR2 8.95E−01 3.81E−02 23.74 3.23295404 13.356071350.81 FALSE 1.29 0.98 NDUFAF6 1.88E−01 3.96E−01 23.75 10.75636072.527813066 1.97 FALSE NA NA HSD17B14 6.74E−01 1.51E−01 23.760.434598607 25.17510233 1.72 FALSE 1.16 1.55 GPC3 5.12E−01 1.16E−0223.81 28.39313231 −5.63330805 1.17 TRUE 1.77 1.50 PGAM1 1.41E−011.70E−01 23.81 1.192883052 16.0990166 0.93 FALSE 2.85 2.14 C16orf887.89E−01 7.76E−02 23.89 16.56262336 4.404648103 2.01 FALSE 2.80 0.95MSTO1 6.69E−01 1.41E−01 23.89 4.649573196 15.34721826 2.10 FALSE 2.662.24 TSTA3 3.55E−01 3.22E−01 23.94 3.15100581 16.37853925 2.68 FALSE1.80 2.04 UBAP2L 1.55E−01 5.08E−01 23.97 1.815656305 18.64832265 1.97FALSE 5.67 2.94 C1orf198 9.00E−01 5.96E−04 24.06 3.577483523 22.43332890.58 FALSE 0.91 0.42 MAP1LC3A 9.31E−01 1.21E−01 24.10 3.10425512716.39766697 0.32 FALSE −0.39 −0.34 ISG20L2 4.82E−02 7.63E−02 24.215.979207631 5.765211543 2.61 FALSE 3.40 2.02 PHB2 2.37E−01 6.02E−0124.23 5.049553302 6.970041892 0.90 FALSE 2.24 0.97 SETDB1 4.06E−012.32E−01 24.24 7.633068319 13.36593165 0.89 FALSE 2.05 1.23 MRPL157.82E−01 2.79E−01 24.35 14.96676665 0.581390132 0.63 FALSE 0.78 0.45MRPS16 1.40E−02 1.52E−01 24.39 2.641804679 22.5309111 1.30 FALSE 2.351.47 EIF2S3 3.38E−01 1.48E−01 24.47 1.156094853 13.37497764 1.03 FALSE−0.58 −0.91 ACAA2 4.20E−01 3.56E−01 24.48 15.52042436 6.988920199 3.83FALSE 1.04 0.47 TYRP1 2.10E−01 6.41E−01 24.53 −1.989085889 12.274486580.37 TRUE 4.01 3.07 HDAC2 6.57E−01 4.07E−02 24.61 10.462425061.208332687 1.89 FALSE −0.33 −0.87 PIH1D1 4.87E−01 3.31E−01 24.706.126480848 5.911744655 0.82 FALSE 0.33 −0.49 KLHDC3 5.70E−01 5.28E−0124.75 22.33788991 0.664001516 1.15 FALSE 0.64 0.44 CBX5 3.09E−011.03E−01 24.89 9.713726735 −0.310128775 1.22 FALSE 0.56 0.91 GLOD44.51E−01 5.04E−01 25.00 7.219301782 20.68994046 1.38 FALSE 0.87 0.44ZNF146 8.68E−01 5.38E−02 25.03 9.080673457 6.819875157 1.59 FALSE −1.63−1.82 NOP2 5.64E−01 7.22E−02 25.08 11.43230613 4.158086545 1.14 FALSE4.00 1.65 TTC39A 7.10E−01 2.95E−01 25.13 0.597290641 46.83816572 1.24FALSE 2.76 2.75 SRSF7 7.30E−01 9.45E−02 25.21 8.342370883 11.021461982.23 FALSE NA NA LHFPL3-AS1 1.04E−01 8.00E−01 25.24 −1.251309555100.2514925 1.11 FALSE NA NA ARHGDIB 6.71E−01 4.56E−01 25.26 5.905664136−2.352194126 0.72 FALSE −3.46 0.38 CYC1 5.00E−01 3.51E−01 25.326.635231365 2.419386869 0.90 FALSE 1.34 1.07 ECH1 4.73E−01 6.23E−0125.36 1.907500873 11.830827 0.92 FALSE 0.78 0.46 DECR1 2.26E−01 3.03E−0125.39 7.937262507 8.725112226 1.50 FALSE 0.45 0.66 SET 6.42E−01 2.79E−0125.45 4.492009689 −0.831007838 0.72 TRUE 0.94 1.17 MTG1 9.69E−021.90E−01 25.55 2.947736591 23.27249486 1.53 FALSE 1.11 1.61 KIAA00201.64E−01 7.24E−02 25.57 9.60401931 20.8418105 3.17 FALSE −0.87 −0.80TMEM204 6.97E−01 1.75E−02 25.57 −1.118931472 23.08239672 1.12 FALSE 1.251.89 TPX2 5.81E−01 1.52E−02 25.77 6.760853407 2.94437134 1.10 FALSE 2.341.50 H19 5.96E−01 5.55E−02 25.91 23.61054168 −2.06812358 1.12 FALSE 2.091.92 CCT3 7.28E−01 3.59E−02 26.21 1.163472086 9.119575759 0.97 TRUE 2.531.55 MAZ 1.20E−01 6.09E−01 26.28 2.123839678 31.48304112 1.74 FALSE 3.122.51 UBE2T 4.62E−01 5.78E−03 26.39 5.398937996 6.61326871 1.01 FALSE1.24 0.33 FES 6.27E−01 4.01E−01 26.47 3.382276111 18.36046107 0.93 FALSE−0.51 0.43 VPS72 6.31E−01 5.39E−02 26.53 3.539245137 18.53462929 2.49FALSE 2.18 0.88 GAGE2A 8.47E−01 5.06E−01 26.66 26.34578486 −13.968624670.74 FALSE −1.31 −2.19 TUFM 2.00E−01 4.80E−01 26.80 8.012399216.859267668 1.22 FALSE 3.70 2.18 ARHGAP4 5.56E−01 4.68E−01 26.840.711358224 23.06729832 0.64 FALSE −2.39 0.44 CCT2 4.07E−01 1.05E−0126.85 10.05194913 1.601942797 1.94 TRUE −0.36 −1.08 CDK1 2.44E−011.98E−01 26.89 8.429749705 4.579575038 1.09 FALSE 0.50 0.37 TIMM227.29E−01 2.48E−01 27.00 11.76212824 8.691671484 2.40 FALSE 2.25 1.64UHRF1 1.91E−01 7.94E−02 27.05 11.51644137 3.212636756 0.59 FALSE 1.581.23 PTGDS 1.81E−01 3.97E−02 27.10 1.823688286 19.02431831 1.54 FALSE−0.48 2.64 RPSA 7.46E−02 4.38E−01 27.28 0.915567272 24.13544158 1.43FALSE 0.54 0.86 RPL29 4.20E−02 3.07E−01 27.64 2.972270992 52.834684340.64 FALSE 0.92 0.78 CECR5 6.73E−02 2.16E−01 27.64 13.822315 12.734665811.44 FALSE 2.59 1.35 HENMT1 7.20E−01 1.17E−01 27.70 10.62719382.355018229 0.47 FALSE NA NA SAMM50 7.10E−01 2.57E−02 27.73 5.61234038828.91606056 1.76 FALSE 3.26 1.17 PPAP2C 6.46E−01 1.02E−01 27.8813.08854512 13.32659581 0.63 FALSE 0.96 0.90 TRAF7 4.61E−02 5.61E−0128.04 6.064625478 9.78220488 1.01 FALSE 2.88 2.20 NPL 5.63E−01 3.07E−0128.07 0.304899232 41.13940087 0.67 FALSE 0.45 0.83 NOSIP 7.94E−012.53E−01 28.19 7.332252555 5.086418955 0.83 FALSE 0.65 0.73 UBE2C4.15E−01 8.94E−03 28.23 9.149834832 3.062410476 1.03 FALSE 2.08 1.46RPL13A 1.11E−01 4.09E−01 28.31 1.051589093 14.62933637 0.79 TRUE −0.320.45 TUBA1B 4.48E−01 4.72E−01 28.35 7.11176895 4.011979889 1.64 FALSE2.11 2.10 MPZL1 9.84E−01 1.31E−02 28.40 2.648030647 32.98716246 1.58FALSE 1.65 0.74 LINC00439 8.10E−01 7.13E−02 28.43 11.2244352 1.2895307920.65 FALSE NA NA NCBP1 5.60E−01 4.63E−01 28.57 5.488108351 20.395013883.16 FALSE 0.36 0.42 SMIM15 5.85E−01 3.47E−01 28.60 6.239127598.28036221 0.36 FALSE NA NA UQCRH 5.30E−01 2.97E−01 28.67 22.1641541−0.337282219 1.11 TRUE 1.42 0.46 APP 7.36E−01 7.63E−02 28.72 9.59912902318.69879539 0.43 FALSE 0.36 0.59 ADSL 3.60E−01 4.89E−02 28.74 6.1143210924.62523135 2.18 FALSE 0.56 −0.90 UCK2 4.01E−01 2.08E−01 28.959.052578861 3.566943066 1.07 FALSE 1.80 0.82 TP53I11 7.37E−01 2.79E−0129.06 17.31232856 3.337087794 1.14 FALSE 1.19 2.37 GPATCH4 5.94E−011.85E−01 29.10 12.26517954 11.03023118 1.48 FALSE 0.81 −0.34 C20orf1125.78E−01 1.41E−01 29.13 30.6975856 2.959060323 3.54 FALSE −0.60 −0.46RPL17 4.53E−02 4.62E−01 29.30 5.134546488 21.69127968 1.41 FALSE −0.53−0.65 BGN 4.65E−01 1.30E−01 29.51 11.64816463 0.49018527 1.80 FALSE 1.452.25 BCCIP 4.09E−01 9.82E−02 29.59 5.686214848 17.77614765 1.64 FALSE−0.86 −1.00 CALM3 6.27E−01 2.48E−01 29.70 5.470474648 20.69905116 0.69FALSE 2.37 2.29 FAM178B 7.63E−01 1.55E−02 29.73 −0.777212747 24.887916090.43 FALSE 0.77 0.55 PAICS 3.90E−01 5.41E−01 29.76 3.31203265924.26869834 1.74 FALSE 1.52 0.48 TSR1 5.73E−01 1.43E−01 29.9411.25783989 1.502635952 2.19 FALSE 0.94 0.32 DDX21 5.48E−02 4.82E−0129.97 3.930072862 12.6570417 0.62 FALSE −0.65 −0.52 METAP2 4.28E−014.93E−01 30.02 11.00208454 8.139440078 1.75 FALSE −0.92 −2.24 TPM11.25E−01 3.35E−01 30.15 16.47245443 3.780545609 1.38 FALSE −0.36 0.45CHP1 1.28E−03 7.38E−01 30.25 −0.401031609 22.86929931 0.99 FALSE NA NADDX50 4.87E−02 6.45E−01 30.29 4.624495525 16.84678101 1.01 FALSE −2.74−2.56 RPL30 3.28E−01 5.91E−01 30.39 14.92031239 3.618436257 0.77 FALSE−0.63 −0.43 FBLN2 3.82E−01 2.03E−03 30.66 7.803353827 7.695710285 1.55FALSE 0.87 1.75 BANCR 1.42E−01 5.48E−01 30.82 3.861608173 8.4027341730.46 FALSE NA NA SCIN 6.93E−01 5.54E−02 31.02 −2.738650819 81.946582721.03 FALSE 0.70 1.60 C19orf48 7.07E−01 2.88E−01 31.11 6.1905446096.03867728 1.17 FALSE 2.31 1.10 RPL5 2.21E−01 4.46E−01 31.16 6.7520079166.997915457 1.15 FALSE −0.84 −1.18 SCD 1.21E−01 6.04E−01 31.17−18.67992188 88.98473766 0.55 TRUE 0.71 0.59 MDH2 2.90E−01 2.00E−0131.18 7.906322813 3.895302932 1.76 TRUE 2.21 0.97 PRAME 4.80E−014.11E−01 31.19 9.259758737 33.89245342 2.16 FALSE 0.32 −0.41 HNRNPA13.07E−01 1.30E−01 31.31 4.371453406 3.269972055 1.46 TRUE −0.33 −0.43SCNM1 2.19E−01 1.06E−01 31.31 3.806661745 7.463417038 1.35 TRUE 1.130.51 TUBB 2.22E−01 2.35E−01 31.61 3.915227069 4.379109031 1.16 TRUE 2.411.48 KLHDC8B 1.33E−01 4.28E−01 31.64 2.496474168 41.31550213 1.51 FALSE2.20 2.37 ASAP1 2.03E−01 3.78E−01 31.68 3.393690401 25.61527297 1.04FALSE 0.31 1.36 CD68 1.98E−01 3.79E−01 31.75 1.979897879 50.211968290.57 FALSE −1.06 0.58 ANP32E 4.92E−01 2.24E−01 31.96 12.318966956.597294926 0.68 FALSE −1.10 −0.82 ITM2C 9.63E−01 1.39E−02 32.088.323594178 9.180730963 0.37 FALSE 0.59 0.59 VDAC2 8.01E−02 3.76E−0132.24 −1.012942241 29.46398783 0.83 FALSE 1.61 0.74 EGFL8 4.52E−011.29E−01 32.55 12.73725487 42.56456272 1.01 FALSE 1.36 1.54 RPS111.39E−01 2.94E−01 32.62 6.172582657 42.70200252 0.39 FALSE 0.32 0.36GRWD1 4.38E−01 5.24E−01 32.83 10.9143261 5.020040199 1.24 FALSE 5.623.78 CS 1.65E−01 7.41E−01 33.27 6.422065041 17.1233515 2.24 FALSE 5.082.61 FAM92A1 1.80E−01 1.18E−02 33.62 23.12776574 3.344554005 0.74 FALSE−1.33 −2.16 NDUFS2 7.10E−01 9.12E−02 34.33 3.683553625 26.49606832 2.86FALSE 1.87 0.56 PPA1 1.68E−02 7.35E−01 34.57 4.191072237 36.244609641.33 FALSE −3.57 −1.51 THOC5 4.22E−01 3.11E−01 34.76 23.722111488.655594417 1.61 FALSE 0.56 −0.39 NF2 2.21E−01 4.46E−01 35.445.935951855 29.68303947 1.87 FALSE 3.24 2.59 SMS 3.48E−01 3.36E−01 35.4510.57117775 7.554385933 3.53 FALSE 0.82 0.45 MARCKS 2.18E−01 8.98E−0135.55 1.393466011 26.71905725 0.42 TRUE −0.60 −0.35 TRPM1 2.73E−024.37E−01 35.72 −18.29374495 70.90187013 0.92 TRUE 3.10 2.37 RPL10A4.87E−02 3.71E−01 35.75 6.395271832 19.89595719 1.43 FALSE 0.56 0.44LYPLA1 3.39E−01 5.06E−01 36.15 10.23638354 8.320184641 1.87 FALSE −2.44−1.87 FBL 5.03E−01 3.43E−01 36.53 4.637441097 24.85286255 1.35 FALSE2.64 1.65 ZNF286A 9.41E−01 4.19E−02 36.53 14.1424198 2.768284631 1.36FALSE −0.47 −0.77 LIMD2 5.49E−01 1.46E−01 36.60 2.122873767 9.2951022031.17 FALSE −0.75 2.94 TULP4 2.15E−01 8.43E−02 36.72 3.56647539221.11741429 1.35 FALSE 0.87 1.16 TIMM13 5.36E−01 2.65E−01 37.2613.78610742 7.021803959 0.77 FALSE 2.20 1.24 RPAIN 5.60E−01 1.47E−0137.35 20.39074062 4.484947614 1.21 FALSE −0.81 −1.76 RBM34 3.24E−012.16E−01 37.89 2.249744298 18.86752144 2.58 FALSE −1.41 −2.72 AHCY3.78E−01 5.00E−02 38.02 10.5770466 15.46879045 2.09 FALSE 2.49 1.19MLLT11 9.77E−01 1.52E−02 38.08 44.02874412 −1.884444301 0.56 TRUE 0.760.55 MYBBP1A 6.00E−01 2.83E−01 38.23 29.53471619 4.324352219 1.57 FALSE2.43 1.71 AEN 5.21E−02 2.42E−01 38.35 14.49457588 12.69107053 2.30 FALSE3.38 2.32 TRIM28 3.81E−01 3.31E−01 38.48 14.93519938 7.65022211 1.28FALSE 2.93 2.05 NOLC1 2.47E−02 2.92E−01 38.64 8.507240496 23.202487311.84 FALSE 3.61 2.77 SHMT2 2.12E−01 1.72E−01 38.82 7.774111145.099441692 0.97 FALSE 2.62 1.34 TYMS 4.65E−01 1.60E−01 38.855.796612685 6.721259278 1.64 FALSE 2.02 1.91 RPS12 3.71E−02 4.01E−0138.95 6.384081023 4.082782447 1.08 FALSE 0.45 0.39 SORD 2.73E−023.55E−01 38.98 9.939454508 11.49665193 2.10 FALSE 3.16 1.05 RPL74.01E−01 3.36E−01 39.04 11.15340377 3.782743401 1.06 FALSE −0.38 0.30ESRP1 4.44E−01 4.55E−02 39.09 10.06244484 25.10697937 1.20 FALSE 2.421.76 BZW2 6.62E−01 1.05E−01 39.22 21.62172441 26.92442566 0.92 FALSE1.37 0.90 RPL18A 8.24E−02 3.34E−01 39.43 2.878936474 36.69844039 0.51TRUE 1.13 1.24 CA14 3.81E−02 1.81E−01 39.82 −3.998230163 67.430652410.77 FALSE 2.21 1.79 SKP2 9.82E−01 1.14E−02 39.93 21.518688721.283417716 1.83 FALSE 1.68 1.42 DCAF13 4.60E−01 2.72E−01 40.4124.87612305 1.564297695 2.88 TRUE −1.21 −1.72 HMGA1 6.81E−02 6.40E−0140.42 19.74301642 5.936479134 0.83 FALSE 0.84 0.52 KIAA0101 4.38E−015.20E−02 41.14 5.177374736 9.343491776 1.31 FALSE −0.55 −0.59 CTPS18.43E−01 8.35E−02 41.34 24.76379084 7.765650207 1.78 FALSE NA NA PPP2R1A3.35E−01 5.52E−01 42.96 5.321317629 16.81313352 1.23 FALSE 5.38 2.24FBLN1 5.09E−01 4.76E−03 43.12 7.246750299 20.27949953 1.92 FALSE 1.652.28 RNF2 8.06E−01 2.83E−02 43.71 8.672094386 7.736904785 2.07 FALSE−0.38 −0.97 CDCA7 6.15E−01 3.99E−02 43.91 5.924051047 11.47163669 1.55FALSE 0.55 0.97 RPS6 8.53E−02 5.20E−01 43.91 1.692897361 54.0756381 0.83TRUE −1.35 −1.38 ILF2 8.63E−01 1.77E−03 45.26 6.943339213 14.849728171.39 FALSE 1.31 0.79 RPL18 9.66E−02 2.25E−01 45.37 3.11448443448.25066529 0.95 FALSE 1.39 1.27 UQCRFS1 4.79E−01 1.04E−01 45.942.40443746 31.0840894 0.72 FALSE 3.57 1.57 RUVBL2 7.03E−01 3.34E−0146.06 9.456736484 13.39002528 1.57 FALSE 2.93 1.38 RPL26 1.01E−011.65E−01 46.82 16.99198955 14.09396856 0.84 FALSE −2.08 −2.60 RPS271.47E−02 3.83E−01 47.85 6.873462208 48.31694024 0.66 FALSE −0.90 0.30CDKN2A 5.27E−01 6.49E−01 48.20 1.937507613 16.9016692 0.77 TRUE −0.46−0.33 MIR4461 9.23E−01 1.12E−02 48.20 5.488218285 21.56158776 1.49 FALSENA NA TPM2 5.40E−01 2.36E−02 48.33 47.15134153 0.452068271 0.90 TRUE−0.30 0.49 CNRIP1 4.03E−01 5.26E−01 48.87 10.22154305 16.25935254 1.06FALSE −0.36 −0.36 PAFAH1B3 3.38E−01 4.49E−01 49.53 9.15923778528.8635675 1.14 FALSE 1.48 0.86 FAM174B 6.29E−01 2.83E−01 50.0715.22332615 36.22910751 1.63 FALSE 3.44 1.88 USP22 4.57E−01 4.65E−0151.05 32.02385954 8.721171083 1.03 FALSE 2.18 1.05 GTSF1 8.47E−012.11E−01 51.20 89.51451745 −29.39568184 1.35 TRUE −3.43 −1.39 ISYNA15.19E−01 3.37E−01 51.20 8.162255211 38.33773761 3.05 FALSE 1.99 1.79DLL3 8.77E−01 6.01E−02 51.70 14.88708119 20.30775936 3.27 FALSE 3.092.42 TMC6 3.36E−01 5.51E−02 52.13 5.290669679 67.7112702 2.25 FALSE 2.613.47 RPS18 7.25E−02 7.13E−01 52.28 27.56806633 18.63526549 0.69 FALSE0.61 0.32 NREP 6.54E−01 6.71E−03 52.32 66.79439813 −16.67629308 0.68TRUE NA NA RPL21 3.07E−01 2.13E−01 52.38 3.737360847 14.06619494 2.11TRUE −1.10 −1.33 RPS3 5.62E−02 3.60E−01 52.44 10.48799182 69.453711161.37 FALSE 0.97 0.76 RPS5 2.04E−02 3.71E−01 56.38 4.84715055 32.712606560.81 TRUE 1.33 0.83 EIF4A1 7.28E−01 1.85E−01 56.54 12.4417655223.79777896 1.45 FALSE 1.80 0.60 GPI 1.17E−01 3.72E−01 57.12 1.13037112845.76480744 1.30 TRUE 4.46 2.91 BCAN 7.45E−01 2.02E−01 57.20 2.30851440972.97911384 0.48 FALSE 3.07 3.42 FTL 2.64E−01 3.99E−01 57.23 1.20506419475.23699673 1.17 FALSE 0.51 2.31 DCT 3.01E−01 4.11E−01 58.58−1.023830081 123.9360976 0.58 TRUE 1.78 2.06 RPS16 2.08E−01 4.47E−0258.91 5.580237253 61.90003741 1.24 FALSE 0.90 0.61 RPL6 1.02E−015.40E−01 60.07 16.14902123 9.904010704 2.18 TRUE −0.35 −0.63 IDH26.45E−01 1.16E−01 60.71 11.44171851 14.05976702 1.57 FALSE −0.31 1.14H3F3A 3.97E−01 4.63E−01 61.79 14.22533613 3.667893274 1.73 TRUE −0.70−0.70 EIF3K 3.13E−01 9.04E−02 61.83 8.143610635 22.75126648 0.89 FALSE2.25 1.49 SAE1 7.36E−01 1.87E−01 64.08 5.547424178 19.20099815 1.27FALSE 3.78 2.16 TIMM50 6.48E−01 9.10E−02 65.03 5.084853086 35.295380791.29 FALSE 2.94 1.91 RPS24 8.85E−02 3.75E−01 66.05 3.71633030698.77989575 1.30 FALSE −0.64 −0.62 RPL28 1.50E−02 4.21E−01 67.315.83385988 54.9536147 0.71 TRUE 0.99 1.01 MID1  1.41E−O1 5.75E−01 68.4530.60224621 19.98794862 1.40 FALSE 1.53 1.43 MAGEA4 6.31E−01 2.50E−0170.13 154.853259 −37.77268982 0.76 TRUE −1.19 −1.25 SOX4 4.33E−013.28E−01 71.11 26.0610551 13.43061044 2.03 FALSE 1.15 0.82 EIF4EBP24.09E−02 5.48E−01 71.92 4.991883552 41.12087104 1.61 FALSE 0.57 1.03SNAI2 3.83E−01 1.30E−01 75.43 7.149559432 49.17185344 1.36 FALSE 1.351.14 FOXRED2 2.26E−01 4.31E−01 75.45 12.49982549 58.21339609 3.02 FALSE3.28 1.62 RPL13AP5 1.17E−01 2.55E−01 77.82 2.595272255 72.74029977 0.90TRUE NA NA PABPC1 1.84E−01 6.67E−01 79.27 7.945824677 66.88581105 1.76FALSE −0.44 0.64 RPL8 1.61E−01 5.12E−01 79.52 0.613777713 40.160808491.75 TRUE 0.73 1.10 RPS7 1.97E−01 2.87E−01 79.88 12.55655574 40.627112741.62 FALSE −0.52 −0.79 C1QBP 4.72E−01 1.88E−01 84.82 24.3094479714.37047936 1.82 TRUE 1.60 0.63 TP53 5.16E−01 4.69E−01 85.56 32.4495700913.44990773 1.60 TRUE 0.40 0.47 C17orf76-AS1 7.92E−01 9.18E−02 87.516.678852726 62.53860033 1.51 FALSE NA NA PTP4A3 5.09E−01 1.18E−01 94.1218.75491086 26.97417247 3.61 FALSE 1.56 1.83 PFN1 3.26E−01 2.42E−0196.68 20.383459 27.16487933 2.07 FALSE 1.34 2.82 RPLP0 5.66E−02 6.51E−01102.20 8.883720005 57.64453707 1.97 TRUE 1.37 0.73 RPS19 1.31E−013.50E−01 116.49 8.842607397 97.09263286 1.07 TRUE 1.43 1.14 SERPINF11.90E−01 4.68E−01 138.29 45.36545505 71.24671866 3.31 FALSE 0.79 0.87

TABLE 3 Down-regulated and Up-regulated genes post- immunotherapytreatment in malignant cells Down-regulated post-treatment Up-regulatedpost-treatment ABHD2 ITM2B ACAA2 PRDX3 ACSL4 JUNB ADSL PSTPIP2 AHNAKKCNN4 AEN PTGDS AHR KIAA1551 AHCY PTP4A3 AIM2 KLF4 ALDH1B1 RBM34 ANGPTL4KLF6 ARHGEF1 RBM4 ANXA1 LAMB1 ARPC5 RPL10A ANXA2 LAMP2 ATXN10 RPL17 APODLGALS1 ATXN2L RPP30 ATF3 LGALS3BP B4GALT3 RPS3 ATP1A1 LINC00116 BCCIPRPS7 ATP1B3 LOC100127888 BGN RPSA BBX LOXL2 C10orf32 RUVBL2 BCL6 LOXL3C16orf88 SAMM50 BIRC3 LPL C17orf76-AS1 SBNO1 BSG LXN C20orf112 SERPINF1C16orf45 MAGEC2 CDCA7 SKP2 C8orf40 MFI2 CECR5 SLC45A2 CALU MIA CPSF1SMC3 CARD16 MT1E CS SMG7 CAV1 MT1F CTCFL SMS CBFB MT1G CTPS1 SNAI2CCDC109B MT1M DLL3 SORD CCND3 MT1X DTD2 SOX4 CD151 MT2A ECHDC1 SRCAPCD200 NFE2L1 ECHS1 SRSF7 CD44 NFKBIZ EIF4A1 STARD10 CD46 NNMT EIF4EBP2TBXA2R CD47 NOTCH2 EIF6 THOC5 CD58 NR4A1 EML4 TIMM22 CD59 OS9 ENY2TIMM23 CD9 P4HA2 ESRG TMC6 CD97 PDE4B FAM174B TOMM22 CDH19 PELI1 FAM213ATPM1 CERS5 PIGT FBL TSNAX CFB PMAIP1 FBLN1 TSR1 CHI3L2 PNPLA8 FDXR TSTA3CLEC2B PPAPDC1B FOXRED2 TULP4 CLIC4 PRKCDBP FXN UBAP2L COL16A1 PRNP GALTUCHL5 COL5A2 PROS1 GEMIN8 UROS CREG1 PRSS23 GLOD4 VPS72 CRELD1 PSMB9GPATCH4 WDR6 CRYAB PSME1 HDAC2 XPNPEP1 CSPG4 PTPMT1 HMGN3 XRCC5 CST3PTRF HSD17B14 YDJC CTNNAL1 RAMP1 IDH2 ZFP36L1 CTSA RND3 ILF2 ZNF286ACTSB RNH1 ISYNA1 CTSD RPN2 KIAA0020 DCBLD2 S100A10 KLHDC8B DCTN6 S100A6LMCD1 EGR1 SCCPDH LOC100505876 EMP1 SERINC1 LYPLA1 EPDR1 SERPINA3 LZTS2FAM114A1 SERPINE1 MAZ FAM46A SERPINE2 METAP2 FCRLA SLC20A1 MIDI FN1SLC35A5 MIR4461 FNDC3B SLC39A14 MPDU1 FXYD3 SLC5A3 MPZL1 G6PD SMIM3MRPS16 GAA SPARC MSTO1 GADD45B SPRY2 MTG1 GALNS SQRDL MYADM GBP2 STAT1MYBBP1A GEM SUMF1 MYL6B GRAMD3 TAP1 NARS2 GSTM2 TAPBP NCBP1 HLA-ATEKT4P2 NDUFAF6 HLA-C TF NDUFS2 HLA-E TFAP2C NF2 HLA-F TMEM43 NHEJ1HPCAL1 TMX4 NME6 HSP90B1 TNC NNT HTATIP2 TNFRSF10B NOLC1 IFI27L2TNFRSF12A NTHL1 IFI44 TSC22D3 OAZ2 IFI6 TSPAN31 OXA1L IFITM3 UBA7 PABPC1IGF1R UBC PAICS IGFBP3 UBE2L6 PAK1IP1 IGFBP7 XPO7 PFN1 IL1RAP ZBTB20POLR2A ITGA6 ZDHHC5 PPA1 ITGB3 ZMYM6NB PRAME

The signature was down-regulated in resistant tumors for genesassociated with coagulation, apoptosis, TNF-alpha signaling via NFKB(NFKBIZ), Antigen processing and presentation (e.g., MHC-I, HSPA1A),metallothioneins (e.g., MT2A, MT1E) involved in metal storage,transport, and detoxification, and IFNGR2 (Gao et al. Cell 2016).

The signature was up-regulated in resistant tumors for genes associatedwith negative regulation of angiogenesis and MYC targets.

Serine protease inhibitors (SERPINs), which are involved in proteaseinhibition and control of coagulation and inflammation weredifferentially expressed in the signature. Prior studies relate torecurrent SERPINB3 and SERPINB4 mutations in patients who respond toanti-CTLA4 immunotherapy (Riaz et al. NG 2016). SERPINA3, SERPINA1,SERPINE2 were down-regulated in resistant tumors. SERPINF1, SERPINB9were up-regulated in resistant tumors.

The resistance signature also strongly correlated with MHC-I expression(FIG. 22). One of the tumors in the cohort has a wide range of MHC-Iexpression in the malignant cells. Applicants filtered HLA genes fromthe resistance signature, and scored the malignant cells. The malignantcells with the highest resistance scores in this tumor under expressMHC-I.

There are 13 different metallothioneins and 6 of them aremoderately/highly expressed in the melanoma malignant cells. WhenApplicants scored the cells according to this mini-signature separationbetween the treated and untreated samples was observed (FIG. 23).Therefore, a signature only including metallothioneins may be used inthe methods of the present invention.

The Prognostic Value of the Post-Immunotherapy Modules

Applicants discovered that the immunotherapy resistance signature wasalso predictive of survival rates in tumors. The prognostic value of thesignature is significant (P=1.6e-05), even when accounting for T-cellinfiltration scores as shown by analyzing samples in the cancer genomeatlas (TCGA) (FIG. 24). The resistance signature performs better thanother single-cell based signatures in predicting high and low survivalrates (FIG. 25).

To further examine the generalizability of the PIT modules Applicantsanalyzed the bulk gene expression data of melanoma tumors from TheCancer Genome Atlas (TCGA). As Applicants saw at the single-cell level,Applicants find that the genes within each module are co-expressedacross tumors, while the two modules are negatively correlated.Applicants postulated that higher expression of the PIT-up program and alower expression of the PIT-down program might indicate that the tumoris more resilient against immune-mediated clearing, resulting in a moreaggressive disease. To test this hypothesis, Applicants scored eachtumor according to the immunotherapy modules and examined the prognosticvalue of these scores. Indeed, the immunotherapy scores aresignificantly associated with patient survival, such that the expressionof the PIT-up (down) signature is associated with lower (higher)survival rates (FIG. 24, 25).

To examine the significance of this finding Applicants performed thesame analysis with signatures that were previously identified based onthe analysis of the single cell melanoma data (Tirosh et al., Dissectingthe multicellular ecosystem of metastatic melanoma by single-cellRNA-seq. Science. 2016 Apr. 8; 352(6282):189-96). Applicants dividedthese signatures into two groups: (1) malignant signatures—signaturesthat describe the state of the malignant cells, as cell cycle, and theAXL and MITF signatures, which were previously shown to be associatedwith the response to targeted therapy; (2) tumor composition signaturesthat describe a specific cell type or the state of a non-malignant celltype within the tumor microenvironment. None of the malignant signaturesis significantly associated with patient survival, indicating that merevariation across malignant cells is not sufficient to yield suchresults. The cell-type signatures are associated with patient survival,especially those that related to T-cell infiltration, though theirprognostic signal is redundant when accounting for tumor purity. Thelatter is estimated based on CNVs.

Importantly, the prognostic value of the PIT scores is significant evenwhen accounting for the tumor purity and T-cell infiltration scores.Interestingly, the PIT-up (down) scores are negatively (positively)correlated to the T-cell scores, as Applicants further describe herein.Nonetheless, the combination of the PIT and T-cell scores yieldssignificantly more accurate predictions of patient survival compared tothose obtained when using each score separately, indicating that the PITmodules capture tumor properties that cannot be explained just by T-cellinfiltration levels.

The Post-Immunotherapy Modules are Associated with Response to Anti-PD1and Anti-CTLA4 in the Clinic and in Mouse Models

Immunotherapy introduces selective pressures that, in case of anunsuccessful treatment, are likely to increase the abundance ofimmunotherapy-resistant cells. The post-immunotherapy signaturesApplicants derived might capture these resistant cell states, and, assuch, may help to detect innate resistance to anti-PD-1 or anti-CTLA4therapy—pretreatment. To examine this concept Applicants analyzed thegene expression profiles of responding (n=15) and non-responding (n=13)tumors sampled prior to anti-PD-1 therapy. Indeed, the tumors ofresponders overexpressed the PIT-down signature and underexpressed thePIT-up signature, resulting in accurate predictors of response toanti-PD-1 (P=3.38e-02 and 5.5e-04, Area Under the Receiver OperatingCharacteristic Curve (ROC-AUC)=0.91 and 0.77. In another gene expressioncohort of pre-anti-CTLA-4 melanoma tumors the signatures did not yield asignificant separation between the responders and non-responders.Therefore, Applicants set out to test the signatures in a morecontrolled setting of a murine model, in which genetically identicalmice that experienced the same environment and treatment display adichotomous response to anti-CTLA4. Indeed, responders scored higher forthe PIT-down signature and lower for the PIT-up signature, resulting inaccurate predictors of response to anti-CTLA4 in this model (P=1.2e-05,ROC-AUC=0.99). The ITR signature was predictive of eventual outcome inboth mouse and human data. First, in a bulk RNA-Seq study of anti-CTLA4therapy in mouse, the malignant ITR score predicted well non-responderscompared to responders. Applicants analyzed 27 patients associated withanti-PD1 response (Hugo et al., 2016). The malignant ITR wassignificantly lower in pre-treatment samples from patients with completeresponse compared to those with partial or no response (FIG. 26). The(5) complete-responders in the data of Hugo et al. scored lower for thesc-resistance signature compared to the other 22 patients (P=9.37e-04).These results indicate that the signatures identified capture cellstates that are linked to anti-PD1 and anti-CTLA4 resistance.

Genes that were up-regulated in the resistant tumors (single cell) weredown-regulated in CR vs. others (P=9.6e-14) and C/PR vs. NR (NS) (FIG.27). Genes that were down-regulated in the resistant tumors (singlecell) were up-regulated in CR vs. others (P=2.8e-11) and C/PR vs. NR(P=2.8e-03) (FIG. 28).

Associating Melanoma-Cell-Intrinsic States with T-Cell Infiltration andExclusion.

Tumor infiltration with T cells is one of the strongest predictors ofpatient response to immune checkpoint inhibitors in various cancertypes. Understanding the molecular mechanisms that underlie spontaneousT-cell infiltration could aid the development of therapeutic solutionsfor patients with non-inflamed tumors. Applicants leveraged thesingle-cell data and bulk gene expression cohorts of melanoma tumors tomap malignant transcriptional states that are associated with T-cellinfiltration or exclusion.

First, Applicants analyzed the single-cell data to derive a CD8 T-cellsignature, consisting of genes that are primarily expressed by CD8T-cells (Methods). Applicants used this signature to estimate the T-cellinfiltration level of melanoma tumors based on their bulk geneexpression profiles. Applicants show that patients with more T-cellinfiltration, according to this measure, are more likely to respond toanti-CTLA4 and to MAGE-A3 antigen-specific immunotherapy, and havebetter overall survival. Next, Applicants identified based on thesingle-cell data genes that are expressed primarily by malignantmelanoma cells. Applicants then searched for genes that are correlatedwith T cell abundance in the bulk TCGA gene expression cohort, whilerestricting the search only to the malignant-specific genes to derive aninitial T-cell-infiltration signature (T cell exclusion signature(T-ex).

While the initial signature is informative, it is limited for two mainreasons. First, there are only 384 genes that could be confidentlydefined as exclusively expressed by the malignant melanoma cells.Second, it cannot confidently identify genes whose expression in themalignant cells will exclude T-cells. To overcome these limitations,Applicants used the initial T-cell infiltration signature only as ananchor, and searched for genes whose expression level in the individualmalignant cells is positively or negatively correlated to the overallexpression of this initial signature. Applicants defined genes that arestrongly positively (negatively) correlated to the initial signature asthe infiltrated (non-infiltrated) module. Of note, non-malignant cellsexpress most of the genes in these modules, and hence it would have beendifficult to associate them with T-cell infiltration without leveragingthe single-cell data.

The genes in the infiltrated module (exclusion-down) play a major rolein antigen processing and presentation (HLA-AB/C, B2M, TAPBP) andinterferon gamma response (e.g., IFI27, IFI35, IRF4, IRF9, STAT2). Incertain embodiments, the infiltrated module includes the followinggenes: A2M, AEBP1, AHNAK, ANXA1, APOC2, APOD, APOE, ATP1A1, ATP1B1, C4A,CAPN3, CAV1, CD151, CD59, CD63, CDH19, CRYAB, CSPG4, CSRP1, CST3, CTSB,CTSD, DAG1, DDR1, DUSP6, ETV5, EVA1A, FBXO32, FCGR2A, FGFR1, GAA,GATSL3, GJB1, GRN, GSN, HLA-B, HLA-C, HLA-F, HLA-H, IFI35, IGFBP7,IGSF8, ITGA3, ITGA7, ITGB3, LAMP2, LGALS3, LOXL4, LRPAP1, LY6E, LYRM9,MATN2, MFGE8, MIA, MPZ, MT2A, MTRNR2L3, MTRNR2L6, NPC1, NPC2, NSG1,PERP, PKM, PLEKHB1, PROS1, PRSS23, PYGB, RDH5, ROPN1, S100A1, S100A13,S100A6, S100B, SCARB2, SCCPDH, SDC3, SEMA3B, SERPINA1, SERPINA3,SERPINE2, SGCE, SGK1, SLC26A2, SLC5A3, SPON2, SPP1, TIMP1, TIMP2, TIMP3,TM4SF1, TMEM255A, TMX4, TNFSF4, TPP1, TRIML2, TSC22D3, TXNIP, TYR, UBCand WBP2.

The non-infiltrated module (exclusion-up) is mainly enriched with MYCtargets and MYC itself. It also includes STRAP, which is an inhibitor ofTGF-beta signaling, and SMARCA4 (or BRG1)—a subunit of the BAF complexthat has a key role in mediating beta-catenin signaling. The latter hasbeen shown to promote T-cell exclusion in mice. In certain embodiments,the non-infiltrated module includes the following genes: AHCY, BZW2,CCNB1IP1, CCT6A, EEF2, EIF3B, GGCT, ILF3, IMPDH2, MDH2, MYBBP1A, NT5DC2,PAICS, PFKM, POLD2, PTK7, SLC19A1, SMARCA4, STRAP, TIMM13, TOP1MT, TRAP1and USP22.

Interestingly, these results mirror and overlap the PIT signatures. Whenscoring the malignant cells according to these infiltration signaturesApplicants find that the treatment naïve malignant cells scoresignificantly higher for infiltration compared to the post-treatmentmalignant cells. In other words, malignant cells having the ITRsignature have higher exclusion signatures and treatment naïve malignantcells have higher infiltration signatures (FIG. 29). These resultsindicate that cells which survive post-immunotherapy either reside inless infiltrated niches within the tumor or have increased capacity toexclude T-cells from their immediate microenvironment. Not being boundby a theory, malignant cells that survive immunotherapy are either tobegin with are in a T cell excluded TME or became T cell excluding.

Immunotherapy Triggers Significant Transcriptional Changes in CD8T-Cells

Next Applicants set out to map the transcriptional landscape of theimmune cells and examine the association of these states withimmunotherapy. Applicants performed the analysis separately for eachcell type (CD8 T-cells, CD4 T-cells, B-cells, and macrophages). First,Applicants performed an unbiased analysis to explore the main sources ofheterogeneity in melanoma CD8 T-cells. To this end, Applicants appliedPrinciple Component Analysis (PCA) followed by nonlinear dimensionalityreduction (t-distributed stochastic neighbor embedding (t-SNE)).Interestingly, in the first PCs and the t-SNE dimensions, the CD8T-cells are segregated according to their treatment history, such thatpost-treatment cells cluster together and apart from the treatment naïvecells. These findings demonstrate that immunotherapy triggerssignificant transcriptional changes in CD8 T-cells, and highlight twoadditional and orthogonal sources of heterogeneity: one that isattributed to cell cycle, and another that is attributed to theexpression of inhibitory checkpoints (FIG. 30, 31).

Applicants performed supervised analyses to identify the genes andpathways that are differentially expressed in the post-immunotherapy CD8T-cells compared to the treatment naïve cells. The resulting signaturesindicate that the post-treatment CD8 T-cells are more cytotoxic andexhausted, such that naïve T-cell markers are downregulated, while IL-2signaling, T-cell exhaustion and activation-dysfunction pathways are upregulated. Applicants then scored the CD8 T-cells according to these twosignatures, revealing a spectrum of phenotypes also within the PIT andtreatment naive populations, and within the CD8 T-cell population of thesame tumor.

Applicants speculated that this spectrum might be related to clonalexpansion. Clonal expansion occurs when T cells that recognize aspecific (tumor) antigen proliferate to generate discernible clonalsubpopulations defined by an identical T cell receptor (TCR) sequence.Applicants applied TraCeR to reconstruct the TCR chains of the T-cellsand identify cells that are likely to be a part of the same clone(Stubbington et al., Nature Methods 13, 329-332 (2016)). Overall,Applicants identified 113 clones of varying sizes, three of whichconsist of more than 20 cells (FIG. 32). Specifically, Applicants usedthe TcR sequence to determine the clone of each T cell, anddistinguished four categories: treatment naive or ITR, and expanded ornot. Applicants analyzed their gene expression and saw that cells varyin two ways. First CD8 T cells from ITR patients have distinctexpression, and this is especially pronounced in expanded cells. All themajor expanded clones were in ITR samples, and only very few cells wereexpanded in treatment naive patients. These few expanded cells look morelike cells from the treated patients. Similar to results reported inmice there is an expanded population of Bcl6+Tcf7+ cells in the ITRsamples, some also CXCR5+. When Applicants turn to their functionalstate, Applicants observed that across all patients, regardless oftreatment, some cells are more exhausted and others more naive.

These large clones are from post-treatment patients, indicating thatimmunotherapy is triggering T-cell activation and proliferation evenwhen no objective clinical response is observed. Moreover, Applicantsfind that clonal expansion is strongly associated with the PIT scores,not only across all patients, but also when considering only thepost-immunotherapy or treatment naive cells. Next Applicants comparedclonally expanded T-cells to the other T-cells within the same tumor toderive signatures of clonal expansion. By leveraging intra-tumor T-cellheterogeneity in this manner Applicants were able to mitigate theproblem of batch effects. In concordance with the previous resultsApplicants find that genes, which are over (under) expressedpost-immunotherapy, are overrepresented in the up (down) regulatedclonal-expansion module (FIG. 33).

Not being bound by a theory, inhibition of genes, which are underexpressed in the T-cells post immunotherapy, could potentially promoteT-cell survival and expansion in the tumor microenvironment. Indeed,these genes are ranked significantly high in the results of an in-vivoshRNA screen that identified negative regulators of T-cell proliferationand survival in mice tumors (P=4.98e-03). All in all, these resultssuggest that post-immunotherapy T-cells are more activated, even in thiscohort of non-responders.

TABLE 4 Post-immunotherapy state in CD8 T-cells Pathway GenesUp-regulated Zinc TFs ZBTB24, ZNF526, ZNF528, ZNF543, ZNF620, ZNF652,ZSCAN2, ZSCAN22 IFN gamma signaling GBP2, GBP5, IRF1, PTPN2, STAT1 PD1signaling CD3D, CD3E, CD3G, HLA-DQA1, HLA-DQA2, HLA-DRB5, PDCD1Down-regulated Cell cycle BIRC5, BUB1, GMNN, MAD2L1, NDC80, TTN, UBE2C,ZWINT Negative regulators of T-cell CBLB, WNK1, PDCD1survival/proliferation in the TME (Zhou et al. 2014)

Not being bound by a theory, immunotherapy is triggering transcriptionalchanges both in the malignant cells and in the CD8 T-cells. The resultssuggest that the T-cells become more effective, while the malignantcells become more “immune-edited” (e.g., evasion (MHC-1) vs. T-cellexclusion).

Example 3—C-Map Analysis

Drugs that could reduce the resistance signature were analyzed by c-mapanalysis. The analysis showed that the following drugs could reduce theimmunotherapy resistance signature:

-   -   PKC activators;    -   NFKB pathway inhibitors;    -   IGF1R inhibitors; and    -   Reserpine (Used to control high blood pressure & psychotic        symptoms and blocks the vesicular monoamine transporter (VMAT)).

The signature is associated with drug response/effects. There was anassociation between the toxicity of different drugs and their resistancescores (according to the resistance signatures). C-map results indicateddrugs that can sensitize/de-sensitize the cells to immunotherapy. Theresults of this analysis are summarized in Table 5-7.

TABLE 5 Drugs that modulate Gene Signature The correlation between theresistance scores of the cell lines and their sensitivity (IC50) to thepertaining drug (based on the CCLE gene expression and the Garnett etal. Nature 2012) Negative R −> more toxic/selective to theimmuno-resistant cells. Positive R −> less toxic/selective to theimmuno-resistant cells. Drug All.R All.P melanoma.R melanoma.P Pazopanib−0.01 8.62E−01 −0.48 3.27E−02 Shikonin −0.05 4.55E−01 −0.48 3.97E−02Etoposide −0.16 2.02E−02 −0.48 4.05E−02 JNK.9L −0.13 4.97E−02 −0.391.00E−01 GSK.650394 −0.17 1.02E−02 −0.37 1.05E−01 X681640 −0.08 1.56E−01−0.37 1.55E−01 Vinorelbine −0.14 3.68E−02 −0.34 1.54E−01 AZD6482 0.161.46E−02 −0.34 1.56E−01 BIRB.0796 −0.11 6.17E−02 −0.33 2.13E−01NVP.BEZ235 0.00 9.79E−01 −0.30 1.59E−01 Roscovitine 0.03 6.90E−01 −0.304.37E−01 Sunitinib −0.04 6.12E−01 −0.30 4.37E−01 Gemcitabine −0.054.18E−01 −0.29 2.19E−01 Epothilone.B −0.05 4.40E−01 −0.29 2.21E−01 ATRA−0.20 6.16E−04 −0.28 3.14E−01 VX.702 −0.16 6.16E−03 −0.27 2.62E−01 QS11−0.17 7.40E−03 −0.27 2.50E−01 Lapatinib 0.29 4.69E−04 −0.27 4.93E−01BMS.536924 0.32 7.64E−05 −0.25 5.21E−01 Vorinostat −0.37 2.73E−11 −0.253.02E−01 PD.0332991 −0.06 2.82E−01 −0.22 3.75E−01 Parthenolide 0.083.55E−01 −0.22 5.81E−01 AZD.2281 −0.18 1.72E−03 −0.21 3.71E−01 FTI.2770.18 6.29E−03 −0.21 3.85E−01 IPA.3 −0.08 2.14E−01 −0.21 3.75E−01PF.562271 0.05 4.31E−01 −0.20 4.10E−01 PD.173074 −0.18 1.89E−03 −0.193.74E−01 Tipifarnib −0.09 1.93E−01 −0.18 4.33E−01 A.770041 0.24 4.60E−03−0.18 6.44E−01 Z.LLNle.CHO 0.23 5.71E−03 −0.18 6.44E−01 CEP.701 −0.109.73E−02 −0.17 4.75E−01 PAC.1 −0.09 1.52E−01 −0.17 4.76E−01 BI.2536 0.149.25E−02 −0.17 6.78E−01 GW843682X 0.05 5.52E−01 −0.17 6.78E−01Midostaurin 0.06 3.70E−01 −0.16 5.03E−01 Metformin −0.21 9.02E−05 −0.164.24E−01 ZM.447439 −0.09 1.27E−01 −0.14 5.21E−01 Elesclomol 0.072.48E−01 −0.14 6.21E−01 AZD7762 −0.08 1.48E−01 −0.14 5.68E−01 Sorafenib−0.03 7.33E−01 −0.13 7.44E−01 XMD8.85 0.00 9.81E−01 −0.13 7.44E−01BAY.61.3606 −0.05 4.84E−01 −0.13 5.81E−01 BI.D1870 −0.02 7.72E−01 −0.136.41E−01 Doxorubicin −0.03 6.08E−01 −0.11 6.44E−01 DMOG 0.17 1.05E−02−0.10 6.78E−01 BMS.509744 0.08 3.30E−01 −0.10 8.10E−01 Bosutinib −0.053.91E−01 −0.09 7.05E−01 CMK 0.17 3.68E−02 −0.08 8.43E−01 KIN001.135 0.192.39E−02 −0.08 8.43E−01 WZ.1.84 0.21 1.47E−02 −0.08 8.43E−01 AZD8055−0.11 5.79E−02 −0.08 7.33E−01 Paclitaxel 0.18 3.31E−02 −0.07 8.80E−01VX.680 −0.01 9.30E−01 −0.07 8.80E−01 LFM.A13 0.12 5.87E−02 −0.068.11E−01 Methotrexate −0.35 1.27E−09 −0.06 8.42E−01 NU.7441 0.101.02E−01 −0.06 8.39E−01 KU.55933 0.07 2.68E−01 −0.05 8.48E−01 JW.7.52.10.11 1.80E−01 −0.05 9.12E−01 OSI.906 0.06 3.67E−01 −0.05 8.36E−01PD.0325901 0.23 4.75E−05 −0.04 8.51E−01 JNK.Inhibitor.VIII 0.03 6.13E−01−0.04 9.00E−01 Gefitinib −0.02 6.99E−01 −0.03 9.23E−01 BMS.754807 0.018.32E−01 −0.02 9.32E−01 BIBW2992 0.04 5.07E−01 −0.02 9.43E−01 Salubrinal−0.11 1.93E−01 −0.02 9.82E−01 Camptothecin.3 0.03 6.44E−01 −0.019.57E−01 Camptothecin.5 0.03 6.44E−01 −0.01 9.57E−01 A.443654 0.149.40E−02 0.00 1.00E+00 Thapsigargin −0.02 7.97E−01 0.00 9.92E−01NSC.87877 −0.17 1.36E−02 0.01 9.86E−01 BX.795 0.03 6.53E−01 0.019.73E−01 X17.AAG 0.22 2.11E−04 0.03 9.26E−01 Mitomycin.C −0.12 7.40E−020.03 9.11E−01 Temsirolimus −0.12 4.14E−02 0.03 9.13E−01 Docetaxel 0.132.39E−02 0.03 8.84E−01 Cyclopamine 0.01 8.84E−01 0.03 9.48E−01Camptothecin −0.12 3.84E−02 0.04 8.74E−01 Camptothecin.4 −0.12 3.84E−020.04 8.74E−01 GDC0941 0.02 7.65E−01 0.04 8.41E−01 Obatoclax.Mesylate−0.05 4.59E−01 0.05 8.41E−01 CGP.082996 −0.01 9.29E−01 0.07 8.80E−01Bleomycin −0.09 1.92E−01 0.08 7.53E−01 AS601245 −0.03 6.96E−01 0.087.29E−01 Bryostatin.1 −0.01 8.31E−01 0.08 7.29E−01 Embelin −0.019.13E−01 0.09 7.05E−01 AKT.inhibitor.VIII −0.10 1.35E−01 0.09 7.05E−01AP.24534 0.13 6.29E−02 0.09 7.05E−01 RDEA119 0.27 1.44E−06 0.12 5.98E−01Nilotinib −0.09 1.34E−01 0.13 6.00E−01 CGP.60474 0.26 1.85E−03 0.137.44E−01 S.Trityl.L.cysteine 0.00 9.82E−01 0.13 7.44E−01 Erlotinib 0.184.08E−02 0.15 7.08E−01 ABT.888 −0.11 6.48E−02 0.15 5.17E−01 MK.2206−0.09 1.47E−01 0.16 5.14E−01 Dasatinib 0.38 4.98E−06 0.17 6.78E−01MG.132 0.25 2.24E−03 0.17 6.78E−01 PF.02341066 0.14 7.89E−02 0.176.78E−01 Cisplatin −0.01 8.94E−01 0.18 5.12E−01 WH.4.023 0.23 5.25E−030.18 6.44E−01 CI.1040 0.09 1.24E−01 0.20 4.74E−01 SL.0101.1 −0.062.74E−01 0.20 4.56E−01 SB590885 −0.11 5.08E−02 0.21 3.61E−01 A.7696620.06 3.85E−01 0.21 3.85E−01 AZ628 0.17 4.24E−02 0.22 5.81E−01 GSK269962A0.21 1.14E−02 0.22 5.81E−01 MS.275 0.00 9.71E−01 0.22 5.81E−01Cytarabine −0.02 7.54E−01 0.22 4.10E−01 Axitinib −0.19 1.11E−03 0.223.54E−01 Vinblastine −0.07 2.38E−01 0.23 3.91E−01 Bicalutamide 0.027.59E−01 0.24 2.99E−01 PLX4720 0.07 2.41E−01 0.25 3.43E−01 RO.3306 0.116.07E−02 0.25 2.41E−01 AUY922 0.01 9.32E−01 0.26 2.75E−01 GNF.2 0.271.27E−03 0.27 4.93E−01 Lenalidomide −0.11 5.30E−02 0.27 2.69E−01GDC.0449 −0.12 3.17E−02 0.29 2.20E−01 AICAR −0.16 3.97E−03 0.30 1.33E−01AZD6244 0.17 4.19E−03 0.32 1.97E−01 Nutlin.3a −0.09 1.18E−01 0.321.36E−01 Bexarotene −0.01 9.08E−01 0.34 1.41E−01 Imatinib 0.15 6.78E−020.35 3.59E−01 Rapamycin 0.09 2.59E−01 0.35 3.59E−01 GW.441756 −0.091.17E−01 0.35 1.41E−01 ABT.263 −0.12 3.89E−02 0.37 8.43E−02 CHIR.990210.19 3.91E−03 0.38 1.10E−01 Bortezomib 0.36 6.13E−06 0.38 3.13E−01Pyrimethamine 0.09 2.84E−01 0.38 3.13E−01 FH535 −0.24 1.51E−04 0.408.41E−02 AMG.706 −0.04 4.43E−01 0.45 3.14E−02 SB.216763 0.02 7.29E−010.46 4.84E−02 AZD.0530 0.31 1.05E−04 0.53 1.48E−01 PHA.665752 0.182.41E−02 0.68 5.03E−02 NVP.TAE684 0.21 8.27E−03 0.72 3.69E−02

TABLE 6 Top 200 drugs that induce downregulated genes in the signatureType (cp= compound, kd = knock-down, oe = over- Rank Score expression,cc = cmap class) ID Name Description 1 99.95 cc PKC Activator — 2 99.95kd CGS001-10538 BATF basic leucine zipper proteins 3 99.95 kdCGS001-25937 WWTR1 Hippo Signaling 4 99.95 kd CGS001-7483 WNT9AWingless-type MMTV integration sites 5 99.95 kd CGS001-2837 UTS2RUrotensin receptor 6 99.95 kd CGS001-7187 TRAF3 — 7 99.95 kdCGS001-27242 TNFRSF21 Tumour necrosis factor (TNF) receptor family 899.95 kd CGS001-7027 TFDP1 — 9 99.95 kd CGS001-64783 RBM15 RNA bindingmotif (RRM) containing 10 99.95 kd CGS001-8438 RAD54L — 11 99.95 kdCGS001-8624 PSMG1 — 12 99.95 kd CGS001-53632 PRKAG3 AMPK subfamily 1399.95 kd CGS001-5184 PEPD Methionyl aminopeptidase 14 99.95 kdCGS001-4688 NCF2 Tetratricopeptide (TTC) repeat domain containing 1599.95 kd CGS001-11004 KIF2C Kinesins 16 99.95 kd CGS001-22832 KIAA1009 —17 99.95 kd CGS001-10014 HDAC5 Histone deacetylases 18 99.95 kdCGS001-2355 FOSL2 basic leucine zipper proteins 19 99.95 kd CGS001-2864FFAR1 Fatty acid receptors 20 99.95 kd CGS001-51719 CAB39 — 21 99.95 kdCGS001-604 BCL6 BTB/POZ domain containing 22 99.95 kd CGS001-326 AIREZinc fingers, PHD-type 23 99.93 cp BRD-K02526760 QS-11 ARFGAP inhibitor24 99.92 kd CGS001-23224 SYNE2 — 25 99.92 kd CGS001-10267 RAMP1 Receptor(G protein- coupled) activity modifying proteins 26 99.92 kd CGS001-4323MMP14 Matrix metallopeptidase 27 99.92 kd CGS001-9455 HOMER2 — 28 99.92kd CGS001-2852 GPER — 29 99.92 kd CGS001-694 BTG1 — 30 99.91 cc NFKB —Activation 31 99.91 oe ccsbBroad304_00833 IFNG Interferons 32 99.91 oeccsbBroad304_02889 WWTR1 Hippo Signaling 33 99.91 oe ccsbBroad304_00832IFNB1 Interferons 34 99.91 oe ccsbBroad304_00259 CD40 Tumour necrosisfactor (TNF) receptor family 35 99.91 oe ccsbBroad304_05881 BCL2L2Serine/threonine phosphatases/Protein phosphatase 1, regulatory subunits36 99.91 oe ccsbBroad304_05390 DUSP28 Protein tyrosinephosphatases/Class I Cys- based PTPs: Atypical dual specificityphosphatases 37 99.91 oe ccsbBroad304_06021 KLF6 Kruppel-liketranscription factors 38 99.91 oe ccsbBroad304_00954 LYN Src family 3999.91 oe ccsbBroad304_03926 SLC39A8 SLC39 family of metal iontransporters 40 99.89 cp BRD-A52650764 ingenol PKC activator 41 99.89 kdCGS001-54472 TOLLIP — 42 99.89 kd CGS001-26472 PPP1R14B Serine/threoninephosphatases/Protein phosphatase 1, regulatory subunits 43 99.89 kdCGS001-6927 HNF1A Homeoboxes/HNF class 44 99.87 kd CGS001-79724 ZNF768Zinc fingers, C2H2-type 45 99.87 kd CGS001-6915 TBXA2R GPCR/Class A:Prostanoid receptors 46 99.87 kd CGS001-51588 PIAS4 Zinc fingers,MIZ-type 47 99.87 kd CGS001-8974 P4HA2 — 48 99.87 kd CGS001-283455 KSR2RAF family 49 99.86 oe ccsbBroad304_00880 IRF2 — 50 99.86 oeccsbBroad304_00771 HOXA5 Homeoboxes/ANTP class: HOXL subclass 51 99.86oe ccsbBroad304_06260 GATA3 GATA zinc finger domain containing 52 99.84kd CGS001-7106 TSPAN4 Tetraspanins 53 99.84 kd CGS001-93487 MAPK1IP1L —54 99.84 kd CGS001-10112 KIF20A Kinesins 55 99.84 kd CGS001-3784 KCNQ1Voltage-gated potassium channels 56 99.84 kd CGS001-182 JAG1 CDmolecules 57 99.84 kd CGS001-1440 CSF3 Endogenous ligands 58 99.82 cpBRD-K91145395 prostratin PKC activator 59 99.82 cp BRD-K32744045disulfiram Aldehyde dehydrogenase inhibitor 60 99.82 kd CGS001-7525 YES1Src family 61 99.82 kd CGS001-7849 PAX8 Paired boxes 62 99.82 kdCGS001-1845 DUSP3 Protein tyrosine phosphatases/Class I Cys- based PTPs:Atypical dual specificity phosphatases 63 99.82 kd CGS001-1154 CISH SH2domain containing 64 99.81 oe ccsbBroad304_04728 TWIST2 Basichelix-loop-helix proteins 65 99.81 oe ccsbBroad304_02048 BCL10 — 66 99.8kd CGS001-10196 PRMT3 Protein arginine N- methyltransferases 67 99.79 cpBRD-A15079084 phorbol-12- PKC activator myristate-13- acetate 68 99.79kd CGS001-7090 TLE3 WD repeat domain containing 69 99.79 kd CGS001-21ABCA3 ATP binding cassette transporters/subfamily A 70 99.78 ccRibonucleotide — Reductase Inhibitor 71 99.78 kd CGS001-23057 NMNAT2 —72 99.77 oe ccsbBroad304_03232 VPS28 — 73 99.76 kd CGS001-115509 ZNF689Zinc fingers, C2H2-type 74 99.76 kd CGS001-9928 KIF14 Kinesins 75 99.76kd CGS001-3417 IDH1 — 76 99.75 cp BRD-K88429204 pyrimethamineDihydrofolate reductase inhibitor 77 99.75 cp BRD-K25504083cytochalasin-d Actin polymerization inhibitor 78 99.75 cp BRD-K47983010BX-795 IKK inhibitor 79 99.74 kd CGS001-6909 TBX2 T-boxes 80 99.74 kdCGS001-5577 PRKAR2B Protein kinase A 81 99.73 kd CGS001-5469 MED1 — 8299.72 oe ccsbBroad304_07680 NEK6 NIMA (never in mitosis gene a)- relatedkinase (NEK) family 83 99.72 cp BRD-A15010982 HU-211 Glutamate receptorantagonist 84 99.72 cp BRD-K33106058 cytarabine Ribonucleotide reductaseinhibitor 85 99.71 kd CGS001-6857 SYT1 Synaptotagmins 86 99.71 kdCGS001-4482 MSRA — 87 99.71 kd CGS001-8321 FZD1 GPCR/Class F: Frizzledreceptors 88 99.71 kd CGS001-124583 CANT1 — 89 99.71 kd CGS001-8312AXIN1 Serine/threonine phosphatases/Protein phosphatase 1, regulatorysubunits 90 99.71 kd CGS001-8874 ARHGEF7 Rho guanine nucleotide exchangefactors 91 99.68 oe ccsbBroad304_03556 SMU1 WD repeat domain containing92 99.68 oe ccsbBroad304_06557 MAOA Catecholamine turnover 93 99.68 oeccsbBroad304_08282 ATP6V1D ATPases/V-type 94 99.66 kd CGS001-8738 CRADD— 95 99.65 kd CGS001-29890 RBM15B RNA binding motif (RRM) containing 9699.63 kd CGS001-3397 ID1 Basic helix-loop-helix proteins 97 99.63 kdCGS001-26036 ZNF451 Zinc fingers, C2H2-type 98 99.63 kd CGS001-9375TM9SF2 — 99 99.63 kd CGS001-10287 RGS19 Regulators of G-proteinsignaling 100 99.63 kd CGS001-374291 NDUFS7 Mitochondrial respiratorychain complex/Complex I 101 99.63 kd CGS001-51001 MTERFD1 — 102 99.63 oeccsbBroad304_06542 LTBR Tumor necrosis factor receptor superfamily 10399.61 cp BRD-A54632525 BRD- — A54632525 104 99.61 kd CGS001-5654 HTRA1Serine peptidases/Serine peptidases 105 99.61 kd CGS001-2673 GFPT1 — 10699.6 kd CGS001-11057 ABHD2 Abhydrolase domain containing 107 99.58 kdCGS001-4835 NQO2 — 108 99.58 kd CGS001-11329 STK38 NDR family 109 99.58kd CGS001-1666 DECR1 Short chain dehydrogenase/reductasesuperfamily/Classical SDR fold cluster 1 110 99.58 kd CGS001-4299 AFF1 —111 99.58 oe ccsbBroad304_07137 WT1 Zinc fingers, C2H2-type 112 99.55 kdCGS001-22949 PTGR1 — 113 99.55 kd CGS001-2071 ERCC3 Generaltranscription factors 114 99.55 kd CGS001-10668 CGRRF1 RING-type (C3HC4)zinc fingers 115 99.55 kd CGS001-348 APOE Apolipoproteins 116 99.54 oeccsbBroad304_00282 CDKN1A — 117 99.54 oe ccsbBroad304_01010 MGST2Glutathione S-transferases/ Microsomal 118 99.51 cp BRD-K77908580entinostat HDAC inhibitor 119 99.5 kd CGS001-7371 UCK2 — 120 99.5 kdCGS001-5198 PFAS — 121 99.5 kd CGS001-51005 AMDHD2 — 122 99.47 kdCGS001-5188 PET112 — 123 99.47 kd CGS001-25836 NIPBL — 124 99.47 kdCGS001-5891 MOK RCK family 125 99.47 kd CGS001-1994 ELAVL1 RNA bindingmotif (RRM) containing 126 99.45 oe ccsbBroad304_04891 TMEM174 — 12799.44 cp BRD-K73610817 BRD- — K73610817 128 99.44 cp BRD-K65814004diphenyleneiodonium Nitric oxide synthase inhibitor 129 99.44 oeccsbBroad304_01388 RELB NFkappaB transcription factor family 130 99.42kd CGS001-8996 NOL3 — 131 99.42 kd CGS001-64223 MLST8 WD repeat domaincontaining 132 99.41 kd CGS001-929 CD14 CD molecules 133 99.4 oeccsbBroad304_07306 TNFRSF10A Tumour necrosis factor (TNF) receptorfamily 134 99.4 cp BRD-K26818574 BIX-01294 Histone lysinemethyltransferase inhibitor 135 99.4 cp BRD-K92991072 PAC-1 Caspaseactivator 136 99.39 cc ATPase — Inhibitor 137 99.37 kd CGS001-1955 MEGF9— 138 99.37 cp BRD-K93034159 cladribine Adenosine deaminase inhibitor139 99.34 kd CGS001-2063 NR2F6 COUP-TF-like receptors 140 99.33 cpBRD-K50841342 PAC-1 — 141 99.32 cc BCL2 And — Related Protein Inhibitor142 99.32 kd CGS001-54386 TERF2IP — 143 99.32 kd CGS001-1852 DUSP9Protein tyrosine phosphatases/Class I Cys- based PTPs: MAP kinasephosphatases 144 99.32 kd CGS001-1212 CLTB — 145 99.32 kd CGS001-9459ARHGEF6 Rho guanine nucleotide exchange factors 146 99.31 oeccsbBroad304_08010 FBXO5 F-boxes/other 147 99.3 kd CGS001-9643 MORF4L2 —148 99.29 kd CGS001-22827 PUF60 RNA binding motif (RRM) containing 14999.29 kd CGS001-1349 COX7B Mitochondrial respiratory chain complex 15099.26 kd CGS001-79885 HDAC11 Histone deacetylases 151 99.26 kdCGS001-4046 LSP1 — 152 99.25 kd CGS001-3177 SLC29A2 SLC29 family 15399.24 kd CGS001-3326 HSP90AB1 Heat shock proteins/HSPC 154 99.23 kdCGS001-1643 DDB2 WD repeat domain containing 155 99.22 kd CGS001-8986RPS6KA4 MSK subfamily 156 99.22 cp BRD-K26664453 cytochalasin-bMicrotubule inhibitor 157 99.21 cc Aldo Keto — Reductase 158 99.21 oeccsbBroad304_01710 TRAF2 RING-type (C3HC4) zinc fingers 159 99.21 oeccsbBroad304_05941 CBR3 Short chain dehydrogenase/reductasesuperfamily/Classical SDR fold cluster 1 160 99.21 kd CGS001-5096 PCCBCarboxylases 161 99.21 oe ccsbBroad304_06392 HOXB7 Homeoboxes/ANTPclass: HOXL subclass 162 99.18 kd CGS001-22955 SCMH1 Sterile alpha motif(SAM) domain containing 163 99.17 oe ccsbBroad304_00773 HOXA9Homeoboxes/ANTP class: HOXL subclass 164 99.17 kd CGS001-3108 HLA-DMAImmunoglobulin superfamily/C1-set domain containing 165 99.17 oeccsbBroad304_05098 MAGEB6 — 166 99.14 oe ccsbBroad304_01686 TNFAIP3 OTUdomain containing 167 99.13 kd CGS001-7690 ZNF131 BTB/POZ domaincontaining 168 99.13 kd CGS001-23011 RAB21 RAB, member RAS oncogene 16999.13 kd CGS001-5106 PCK2 — 170 99.13 kd CGS001-85315 PAQR8 — 171 99.12oe ccsbBroad304_01858 FOSL1 basic leucine zipper proteins 172 99.12 cpBRD-K23984367 sorafenib — 173 99.12 cp BRD-K72264770 QW-BI-011 Histonelysine methyltransferase inhibitor 174 99.11 kd CGS001-11116 FGFR1OP —175 99.1 kd CGS001-4804 NGFR Tumour necrosis factor (TNF) receptorfamily 176 99.08 kd CGS001-6676 SPAG4 — 177 99.08 kd CGS001-63874 ABHD4Abhydrolase domain containing 178 99.07 oe ccsbBroad304_00389 CTBP1 —179 99.05 kd CGS001-7480 WNT10B Wingless-type MMTV integration sites 18099.05 kd CGS001-80351 TNKS2 Ankyrin repeat domain containing 181 99.05kd CGS001-2264 FGFR4 Type V RTKs: FGF (fibroblast growth factor)receptor family 182 99.05 kd CGS001-1725 DHPS — 183 99.05 kdCGS001-64170 CARD9 — 184 99.03 kd CGS001-6259 RYK Type XV RTKs: RYK 18599.03 kd CGS001-54566 EPB41L4B — 186 99.02 kd CGS001-308 ANXA5 Annexins187 99.01 kd CGS001-5257 PHKB — 188 99 kd CGS001-7764 ZNF217 Zincfingers, C2H2-type 189 99 kd CGS001-5451 POU2F1 Homeoboxes/POU class 19098.98 cp BRD-K30677119 PP-30 RAF inhibitor 191 98.98 kd CGS001-23368PPP1R13B Ankyrin repeat domain containing 192 98.98 cp BRD-A34208323VU-0404997-2 Glutamate receptor modulator 193 98.97 kd CGS001-4601 MXI1Basic helix-loop-helix proteins 194 98.97 kd CGS001-10247 HRSP12 — 19598.95 kd CGS001-8295 TRRAP TRRAP subfamily 196 98.95 kd CGS001-26064RAI14 Ankyrin repeat domain containing 197 98.95 kd CGS001-5710 PSMD4Proteasome (prosome, macropain) subunits 198 98.95 kd CGS001-3312 HSPA8Heat shock proteins/ HSP70 199 98.93 cp BRD-K59456551 methotrexateDihydrofolate reductase inhibitor 200 98.93 kd CGS001-10327 AKR1A1Aldo-keto reductases

TABLE 7 Top 200 drugs that repress upregulated genes in the signatureType (cp = compound, kd = knock-down, oe = over- Rank Score expression,cc = cmap class) ID Name Description 8875 −99.95 kd CGS001-10254 STAM2 —8876 −99.95 kd CGS001-5966 REL NFkappaB transcription factor family 8877−99.95 kd CGS001-4609 MYC Basic helix-loop-helix proteins 8878 −99.95 kdCGS001-2079 ERH — 8879 −99.95 kd CGS001-2683 B4GALT1 Beta 4-glycosyltransferases 8880 −99.95 kd CGS001-406 ARNTL Basichelix-loop-helix proteins 8872 −99.92 cc Aldo Keto — Reductase 8873−99.92 kd CGS001-8644 AKR1C3 Prostaglandin synthases 8874 −99.92 kdCGS001-2863 GPR39 GPCR/Class A: Orphans 8870 −99.91 oeccsbBroad304_03864 OVOL2 Zinc fingers, C2H2-type 8871 −99.91 oeccsbBroad304_08418 FBXL12 F-boxes/Leucine-rich repeats 8866 −99.89 kdCGS001-114026 ZIM3 Zinc fingers, C2H2-type 8867 −99.89 kd CGS001-51021MRPS16 Mitochondrial ribosomal proteins/small subunits 8868 −99.89 kdCGS001-3265 HRAS RAS subfamily 8869 −99.89 kd CGS001-1643 DDB2 WD repeatdomain containing 8864 −99.88 kd CGS001-6337 SCNN1A Epithelial sodiumchannels (ENaC) 8865 −99.88 kd CGS001-4191 MDH2 — 8861 −99.87 kdCGS001-26137 ZBTB20 BTB/POZ domain containing 8862 −99.87 kd CGS001-7227TRPS1 GATA zinc finger domain containing 8863 −99.87 kd CGS001-95 ACY1 —8856 −99.86 oe ccsbBroad304_00832 IFNB1 Interferons 8857 −99.86 oeccsbBroad304_05982 CDX2 Homeoboxes/ANTP class: HOXL subclass 8858 −99.86oe ccsbBroad304_06021 KLF6 Kruppel-like transcription factors 8859−99.86 oe ccsbBroad304_01249 PPARG Peroxisome proliferator- activatedreceptors 8860 −99.86 oe ccsbBroad304_00472 EBF1 — 8854 −99.84 kdCGS001-7185 TRAF1 — 8855 −99.84 kd CGS001-5562 PRKAA1 AMPK subfamily8853 −99.83 kd CGS001-7775 ZNF232 Zinc fingers, C2H2-type 8852 −99.82 kdCGS001-10525 HYOU1 Heat shock proteins/ HSP70 8851 −99.81 oeccsbBroad304_07363 AIFM1 — 8850 −99.79 cp BRD-A81772229 simvastatinHMGCR inhibitor 8847 −99.77 oe ccsbBroad304_00747 HLF — 8848 −99.77 oeccsbBroad304_00487 EGR1 Zinc fingers, C2H2-type 8849 −99.77 oeccsbBroad304_04271 MXD3 Basic helix-loop-helix proteins 8846 −99.76 kdCGS001-5608 MAP2K6 MAPKK: STE7 family 8844 −99.75 cc JAK Inhibitor —8845 −99.75 cp BRD-K91290917 amodiaquine Histamine receptor agonist 8843−99.74 kd CGS001-9296 ATP6V1F ATPases/V-type 8841 −99.71 kd CGS001-6389SDHA Mitochondrial respiratory chain complex 8842 −99.71 kd CGS001-6275S100A4 EF-hand domain containing 8839 −99.68 oe ccsbBroad304_00833 IFNGInterferons 8840 −99.68 oe ccsbBroad304_07117 UGCG Glycosyltransferasefamily 2 domain containing 8838 −99.67 kd CGS001-8031 NCOA4 — 8836−99.66 kd CGS001-7167 TPI1 — 8837 −99.66 kd CGS001-3419 IDH3A — 8835−99.63 kd CGS001-5469 MED1 — 8830 −99.61 cp BRD-K52850071JAK3-Inhibitor-II JAK inhibitor 8831 −99.61 cp BRD-K49049886 CGS-15943Adenosine receptor antagonist 8832 −99.61 kd CGS001-115650 TNFRSF13CTumour necrosis factor (TNF) receptor family 8833 −99.61 kd CGS001-6493SIM2 Basic helix-loop-helix proteins 8834 −99.61 kd CGS001-7803 PTP4A1Protein tyrosine phosphatases/Class I Cys-based PTPs: PRLs 8829 −99.59cc Aurora Kinase — Inhibitor Grp2 8825 −99.58 cp BRD-K37691127hinokitiol Tyrosinase inhibitor 8826 −99.58 kd CGS001-5170 PDPK1 PDK1family 8827 −99.58 kd CGS001-4199 ME1 — 8828 −99.58 kd CGS001-51295ECSIT Mitochondrial respiratory chain complex assembly factors 8822−99.55 kd CGS001-51520 LARS Aminoacyl tRNA synthetases/Class I 8823−99.55 kd CGS001-2538 G6PC — 8824 −99.55 kd CGS001-2059 EPS8 — 8819−99.54 cp BRD-K58299615 RO-90-7501 Beta amyloid inhibitor 8820 −99.54 kdCGS001-3485 IGFBP2 insulin-like growth factor (IGF) binding proteins8821 −99.54 cp BRD-K85606544 neratinib EGFR inhibitor 8813 −99.53 kdCGS001-54472 TOLLIP — 8814 −99.53 kd CGS001-4998 ORC1 ATPases/AAA-type8815 −99.53 kd CGS001-9020 MAP3K14 MAPKKK: STE-unique family 8816 −99.53kd CGS001-355 FAS Tumour necrosis factor (TNF) receptor family 8817−99.53 kd CGS001-10327 AKR1A1 Aldo-keto reductases 8818 −99.53 kdCGS001-178 AGL — 8812 −99.52 cc HOX Gene — 8810 −99.51 cp BRD-A19633847perhexiline Carnitine palmitoyltransferase inhibitor 8811 −99.51 cpBRD-K47105409 AG-490 — 8809 −99.49 oe ccsbBroad304_00706 GTF2B Generaltranscription factors 8806 −99.47 oe ccsbBroad304_05980 CDKN1B — 8807−99.47 kd CGS001-8226 HDHD1 — 8808 −99.47 kd CGS001-5045 FURINSubtilisin 8805 −99.45 oe ccsbBroad304_00772 HOXA6 Homeoboxes/ANTPclass: HOXL subclass 8804 −99.44 kd CGS001-3309 HSPA5 Heat shockproteins/HSP70 8803 −99.43 oe ccsbBroad304_00838 IGFBP5 insulin-likegrowth factor (IGF) binding proteins 8802 −99.4 cp BRD-K92991072 PAC-1Caspase activator 8801 −99.39 kd CGS001-35 ACADS — 8800 −99.38 kdCGS001-3122 HLA-DRA Immunoglobulin superfamily/C1-set domain containing8799 −99.37 cp BRD-K66296774 fluvastatin HMGCR inhibitor 8798 −99.36 kdCGS001-7525 YES1 Src family 8797 −99.35 kd CGS001-57178 ZMIZ1 Zincfingers, MIZ-type 8795 −99.34 kd CGS001-3635 INPP5D Inositolpolyphosphate phosphatases 8796 −99.34 kd CGS001-3416 IDE Pitrilysin8794 −99.33 cp BRD-K07881437 danusertib Aurora kinase inhibitor 8793−99.32 cp BRD-A50675702 fipronil GABA gated chloride channel blocker8792 −99.29 kd CGS001-998 CDC42 — 8791 −99.28 cc PI3K Inhibitor — 8787−99.26 cc DNA-dependent — Protein Kinase 8788 −99.26 cp BRD-K94441233mevastatin HMGCR inhibitor 8789 −99.26 oe ccsbBroad304_02571 TOMM34Tetratricopeptide (TTC) repeat domain containing 8790 −99.26 oeccsbBroad304_01579 SOX2 SRY (sex determining region Y)-boxes 8784 −99.24kd CGS001-5682 PSMA1 Proteasome subunits 8785 −99.24 kd CGS001-53347UBASH3A — 8786 −99.24 kd CGS001-2782 GNB1 WD repeat domain containing8782 −99.23 oe ccsbBroad304_11277 HAT1 Histone acetyltransferases (HATs)8783 −99.23 kd CGS001-4323 MMP14 Matrix metallopeptidase 8780 −99.2 kdCGS001-79142 PHF23 Zinc fingers, PHD-type 8781 −99.2 kd CGS001-2664 GDI1— 8778 −99.19 cp BRD-K48974000 BRD-K48974000 — 8779 −99.19 kdCGS001-4817 NIT1 — 8777 −99.18 kd CGS001-7126 TNFAIP1 BTB/POZ domaincontaining 8775 −99.17 kd CGS001-10497 UNC13B — 8776 −99.17 kdCGS001-57448 BIRC6 Inhibitors of apoptosis (IAP) protein family 8772−99.15 cp BRD-K13514097 everolimus MTOR inhibitor 8773 −99.15 cpBRD-K59331372 SB-366791 TRPV antagonist 8774 −99.15 cp BRD-K78373679RO-3306 CDK inhibitor 8770 −99.13 oe ccsbBroad304_02451 HOXB13Homeoboxes/ANTP class: HOXL subclass 8771 −99.13 kd CGS001-7405 UVRAG —8769 −99.12 cp BRD-K06217810 BRD-K06217810 — 8768 −99.11 cc HMGCRInhibitor — 8765 −99.08 kd CGS001-55781 RIOK2 RIO2 subfamily 8766 −99.08kd CGS001-7026 NR2F2 COUP-TF-like receptors 8767 −99.08 kd CGS001-7994KAT6A Histone acetyltransferases (HATs) 8762 −99.07 oeccsbBroad304_06131 DUSP6 Protein tyrosine phosphatases/Class I Cys-basedPTPs: MAP kinase phosphatases 8763 −99.07 kd CGS001-4916 NTRK3 Type VIIRTKs: Neurotrophin receptor/Trk family 8764 −99.07 oe ccsbBroad304_06394HOXC9 Homeoboxes/ANTP class: HOXL subclass 8761 −99.06 cp BRD-K60623809SU-11652 Tyrosine kinase inhibitor 8758 −99.03 oe ccsbBroad304_03574FBXW7 F-boxes/WD-40 domains 8759 −99.03 kd CGS001-6772 STAT1 SH2 domaincontaining 8760 −99.03 kd CGS001-6768 ST14 Serine peptidases/Transmembrane 8757 −99.02 kd CGS001-64170 CARD9 — 8753 −98.98 oeccsbBroad304_02048 BCL10 — 8754 −98.98 cp BRD-K50836978 purvalanol-a CDKinhibitor 8755 −98.98 kd CGS001-9601 PDIA4 Protein disulfide isomerases8756 −98.98 cp BRD-K46056750 AZD-7762 CHK inhibitor 8751 −98.97 kdCGS001-1936 EEF1D — 8752 −98.97 kd CGS001-8192 CLPP ATPases/AAA-type8750 −98.96 kd CGS001-5211 PFKL — 8749 −98.95 kd CGS001-23476 BRD4Bromodomain kinase (BRDK) family 8746 −98.94 cp BRD-K97399794 quercetinPolar auxin transport inhibitor 8747 −98.94 oe ccsbBroad304_10487 BPHL —8748 −98.94 cp BRD-K64890080 BI-2536 PLK inhibitor 8745 −98.93 kdCGS001-3927 LASP1 — 8742 −98.92 kd CGS001-7541 ZFP161 — 8743 −98.92 kdCGS001-56993 TOMM22 — 8744 −98.92 kd CGS001-1326 MAP3K8 MAPKKK:STE-unique family 8739 −98.91 kd CGS001-55038 CDCA4 — 8740 −98.91 kdCGS001-7840 ALMS1 — 8741 −98.91 cp BRD-A31159102 fluoxetine Selectiveserotonin reuptake inhibitor (SSRI) 8736 −98.89 cc MTOR Inhibitor — 8737−98.89 cc IGF1R Inhibitor — 8738 −98.89 oe ccsbBroad304_01545 SLC3A2SLC3 family 8735 −98.88 cp BRD-A75769826 SDM25N Opioid receptorantagonist 8733 −98.87 cc EGFR Inhibitor — 8734 −98.87 cp BRD-K64881305ispinesib Kinesin-like spindle protein inhibitor 8731 −98.8 oeccsbBroad304_00100 RHOA — 8732 −98.8 cp BRD-K05350981 oligomycin-cATPase inhibitor 8730 −98.79 kd CGS001-949 SCARB1 — 8729 −98.78 kdCGS001-2114 ETS2 ETS Transcription Factors 8728 −98.77 cp BRD-K73610817BRD-K73610817 — 8725 −98.74 kd CGS001-166793 ZBTB49 BTB/POZ domaincontaining 8726 −98.74 kd CGS001-55176 SEC61A2 — 8727 −98.74 kdCGS001-8313 AXIN2 — 8723 −98.73 cp BRD-A81177136 KN-62Calcium-calmodulin dependent protein kinase inhibitor 8724 −98.73 kdCGS001-8792 TNFRSF11A Tumour necrosis factor (TNF) receptor family 8722−98.72 kd CGS001-10600 USP16 Ubiquitin- specific peptidases 8720 −98.71kd CGS001-117289 TAGAP Rho GTPase activating proteins 8721 −98.71 kdCGS001-11230 PRAF2 — 8717 −98.7 oe ccsbBroad304_06257 GATA2 GATA zincfinger domain containing 8718 −98.7 cp BRD-K55070890 thiothixene — 8719−98.7 cp BRD-K09499853 KU-0060648 DNA dependent protein kinase inhibitor8715 −98.69 kd CGS001-6777 STAT5B SH2 domain containing 8716 −98.69 kdCGS001-5184 PEPD Methionyl aminopeptidase 8710 −98.66 oeccsbBroad304_06639 NFYB — 8711 −98.66 cp BRD-K68065987 MK-2206 AKTinhibitor 8712 −98.66 kd CGS001-55872 PBK TOPK family 8713 −98.66 kdCGS001-1482 NKX2-5 Homeoboxes/ANTP class: NKL subclass 8714 −98.66 oeccsbBroad304_06393 HOXC4 Homeoboxes/ANTP class: HOXL subclass 8709−98.62 cc NFKB Pathway — Inhibitor 8705 −98.61 kd CGS001-6256 RXRARetinoid X receptors 8706 −98.61 kd CGS001-8833 GMPS — 8707 −98.61 kdCGS001-2021 ENDOG — 8708 −98.61 oe ccsbBroad304_01291 MAP2K6 MAPKK: STE7family 8703 −98.6 oe ccsbBroad304_11796 ULK3 Unc-51-like kinase (ULK)family 8704 −98.6 kd CGS001-5524 PPP2R4 Serine/threoninephosphatases/Protein phosphatase 2, regulatory subunits 8702 −98.59 kdCGS001-27 ABL2 Abl family 8701 −98.58 kd CGS001-54623 PAF1 — 8699 −98.57kd CGS001-11105 PRDM7 Zinc fingers, C2H2-type 8700 −98.57 oeccsbBroad304_08681 ADCK3 ABC1-B subfamily 8697 −98.52 kd CGS001-5777PTPN6 Protein tyrosine phosphatases 8698 −98.52 cp BRD-K02130563panobinostat HDAC inhibitor 8696 −98.51 kd CGS001-4223 MEOX2Homeoboxes/ANTP class: HOXL subclass 8694 −98.5 oe ccsbBroad304_00498ELK1 ETS Transcription Factors 8695 −98.5 kd CGS001-47 ACLY — 8693−98.48 kd CGS001-5434 POLR2E RNA polymerase subunits 8692 −98.47 kdCGS001-4351 MPI — 8691 −98.45 kd CGS001-5710 PSMD4 Proteasome (prosome,macropain) subunits 8687 −98.43 cc HSP90 Inhibitor — 8688 −98.43 kdCGS001-6259 RYK Type XV RTKs: RYK 8689 −98.43 oe ccsbBroad304_08879CASD1 — 8690 −98.43 oe ccsbBroad304_00283 CDKN2C Ankyrin repeat domaincontaining 8686 −98.42 kd CGS001-29957 SLC25A24 Mitochondrial nucleotidetransporter subfamily 8683 −98.41 kd CGS001-3312 HSPA8 Heat shockproteins/HSP70 8684 −98.41 cp BRD-K36740062 GSK-1070916 Aurora kinaseinhibitor 8685 −98.41 cp BRD-K98548675 parthenolidc NFkB pathwayinhibitor 8681 −98.39 kd CGS001-527 ATP6V0C ATPases/V-type 8682 −98.39kd CGS001-513 ATP5D ATPases/F-type 8678 −98.38 oe ccsbBroad304_02864PRDX5 — 8679 −98.38 oe ccsbBroad304_00817 IDH2 — 8680 −98.38 oeccsbBroad304_03232 VPS28 — 8677 −98.37 kd CGS001-481 ATP1B1ATPases/P-type 8676 −98.35 kd CGS001-3113 HLA-DPA1 Immunoglobulinsuperfamily/C1-set domain containing 8672 −98.34 cp BRD-K06147391telenzepinc Acetylcholine receptor antagonist 8673 −98.34 cpBRD-K78122587 NNC-55-0396 T-type calcium channel blocker 8674 −98.34 cpBRD-K14618467 IKK-16 IKK inhibitor 8675 −98.34 kd CGS001-26574 AATF —

Applicants can also identify novel immunotherapy targets by looking forgenes which are co-regulated with the immune-checkpoints (PDCD1, TIGIT,HAVCR2, LAG3, CTLA4) in CD4 and CD8 T-cells. For example, Applicantsfound CD27, an immune checkpoint and the target of an experimentalcancer treatment (Varlilumab). The results of this analysis are for thetop 200 genes summarized in Table 8.

TABLE 8 Top 200 genes that are co-regulated with immune-checkpointsCD8.R CD8.P CD4.R CD4.P PDCD1 0.66  1.59E−215 0.60  1.03E−119 CTLA4 0.63 4.88E−193 0.65  2.60E−145 TIGIT 0.63  1.11E−191 0.73  8.03E−204 HAVCR20.62  1.39E−183 0.32 7.85E−30 LAG3 0.55  7.66E−136 LYST 0.42 5.22E−760.26 1.67E−20 CD8A 0.40 3.93E−66 −0.08 0.007523193 TNFRSF9 0.39 1.13E−640.09 0.001435974 CD27 0.39 6.38E−64 0.22 3.99E−15 FAM3C 0.34 1.47E−48CXCL13 0.34 1.47E−47 0.27 2.41E−21 SP47 0.33 1.43E−46 0.11 0.000202982CBLB 0.33 7.16E−46 0.20 1.64E−12 SNX9 0.33 1.35E−45 0.11 6.89E−05 SIRPG0.33 5.21E−45 0.33 2.62E−31 TNFRSF1B 0.33 1.09E−44 0.22 3.31E−15 FCRL30.32 1.36E−41 0.26 9.32E−20 VCAM1 0.31 3.44E−41 DGKH 0.31 1.67E−39 PRDM10.30 3.07E−38 0.20 2.24E−12 IGFLR1 0.30 6.12E−38 0.21 7.66E−14 ETV1 0.301.03E−37 RGS1 0.30 4.15E−37 0.30 3.35E−27 WARS 0.30 1.32E−36 0.144.98E−07 MYO7A 0.30 3.10E−36 ITM2A 0.29 2.65E−35 0.31 1.30E−27 GBP2 0.291.24E−34 0.21 3.93E−13 ENTPD1 0.28 1.21E−33 0.12 4.17E−05 TOX 0.282.39E−32 0.44 2.27E−58 DUSP4 0.28 2.48E−32 0.36 1.94E−38 TP53INP1 0.287.24E−32 0.23 2.04E−16 GAPDH 0.28 1.57E−31 0.36 1.21E−37 DFNB31 0.276.10E−31 ATHL1 0.27 1.19E−30 0.01 0.71761873  TRAF5 0.27 2.83E−30 0.100.000897484 CLEC2D 0.27 5.88E−30 −0.02 0.535704689 SLA 0.26 6.03E−290.20 1.37E−12 CCL3 0.26 1.26E−28 0.04 0.161249379 IL6ST 0.26 2.25E−280.02 0.440674275 PCED1B 0.26 3.01E−28 0.21 3.65E−13 RAB27A 0.26 3.09E−280.13 6.49E−06 CD7 0.26 3.23E−28 0.06 0.049851187 ICOS 0.25 3.99E−27 0.312.87E−28 FUT8 0.25 1.41E−26 0.10 0.000314275 RNF19A 0.25 4.44E−26 0.291.51E−24 TBC1D4 0.25 1.16E−25 0.31 6.28E−29 FABP5 0.25 1.63E−25 0.182.19E−10 B1 0.24 3.89E−24 0.24 9.16E−18 TTN 0.24 6.97E−24 SRGN 0.249.35E−24 0.36 1.61E−37 SARDH 0.24 2.50E−23 0.19 3.48E−11 IFNG 0.243.00E−23 0.14 4.84E−07 INPP5F 0.23 3.38E−23 0.14 4.75E−07 RGS2 0.234.50E−23 0.18 7.21E−10 CD38 0.23 5.61E−23 0.15 1.54E−07 ID3 0.231.34E−22 0.05 0.066457964 PHLDA1 0.23 1.35E−22 0.11 0.000184209 TIMD40.23 3.53E−22 PAM 0.23 3.69E−22 0.28 2.82E−23 PTMS 0.23 1.99E−21 CXCR60.22 6.84E−21 0.26 6.42E−20 LBH 0.22 1.48E−20 0.18 3.85E−10 PRF1 0.221.90E−20 0.09 0.001065922 ASB2 0.22 1.90E−20 0.29 8.22E−25 KIR2DL4 0.222.29E−20 STAT3 0.22 4.75E−20 0.05 0.063080818 GLDC 0.22 5.92E−20MIR155HG 0.22 8.11E−20 0.15 9.54E−08 CD8B 0.22 1.10E−19 −0.14 2.30E−06CD200 0.22 1.25E−19 0.25 1.49E−18 CD2BP2 0.21 1.79E−19 0.17 5.47E−09CD84 0.21 2.59E−19 0.11 0.000105368 CD2 0.21 3.46E−19 0.32 5.24E−31UBE2F 0.21 3.72E−19 0.06 0.035820564 TNS3 0.21 6.38E−19 ATXN1 0.211.35E−18 HNRPLL 0.21 1.51E−18 0.26 1.96E−20 FKBP1A 0.21 2.34E−18 0.192.16E−11 GALM 0.21 2.95E−18 0.19 3.98E−11 TOX2 0.20 6.98E−18 0.352.14E−35 AFAP1L2 0.20 1.90E−17 GEM 0.20 2.64E−17 0.16 5.19E−08 HSPB10.20 2.75E−17 0.09 0.002636939 CCL3L3 0.20 3.71E−17 CADM1 0.20 3.76E−17GFOD1 0.20 3.88E−17 SH2D2A 0.20 3.90E−17 0.13 6.62E−06 PKM 0.20 4.16E−170.26 1.71E−19 HAPLN3 0.20 9.31E−17 −0.02 0.483961847 MTSS1 0.20 1.03E−16ZNF79 0.20 1.79E−16 0.03 0.275651913 EID1 0.19 2.53E−16 0.09 0.003034835ZBED2 0.19 2.96E−16 0.14 1.69E−06 PTPN6 0.19 1.31E−15 0.04 0.210702886HMOX1 0.19 1.51E−15 SAMSN1 0.19 1.97E−15 0.10 0.00025252  SIT1 0.192.34E−15 0.08 0.007781849 CCDC64 0.19 2.65E−15 0.09 0.000993524 PTPN70.19 4.49E−15 0.25 3.66E−18 NDFIP2 0.19 6.66E−15 0.17 6.39E−09 CD74 0.197.63E−15 0.28 1.23E−22 CREM 0.18 1.94E−14 0.05 0.106041668 IRF4 0.181.98E−14 0.16 4.09E−08 ARNT 0.18 2.23E−14 0.10 0.000571869 TRPS1 0.182.93E−14 ZC3H7A 0.18 3.28E−14 0.14 1.33E−06 RHOB 0.18 3.58E−14 ASXL20.18 3.99E−14 ITGA4 0.18 4.04E−14 0.08 0.008613713 CCL4L2 0.18 5.53E−140.11 0.000238679 CCL4L1 0.18 5.69E−14 0.11 0.000238679 IGF2R 0.181.06E−13 SOD1 0.18 1.26E−13 0.18 4.99E−10 SYNGR2 0.18 1.31E−13 0.110.00010303  PDE3B 0.18 1.38E−13 −0.11 0.000178183 IFI16 0.18 1.43E−130.20 5.81E−12 PDE7B 0.18 1.46E−13 SLC2A8 0.18 1.59E−13 FYN 0.17 2.58E−130.23 2.55E−16 ARID5B 0.17 4.06E−13 0.22 2.72E−15 NFATC1 0.17 4.72E−130.10 0.000521207 TPI1 0.17 4.96E−13 0.17 2.07E−09 DTHD1 0.17 6.29E−13CD3E 0.17 7.13E−13 0.03 0.271016862 CRIM1 0.17 7.24E−13 TMEM155 0.171.02E−12 INPP4B 0.17 1.66E−12 −0.06 0.035577188 OSBPL3 0.17 1.74E−120.16 4.35E−08 LIMS1 0.17 1.76E−12 0.17 1.29E−09 KCNK5 0.17 1.76E−12KLRC2 0.17 2.17E−12 RGS4 0.17 3.04E−12 ACP5 0.17 3.13E−12 0.19 5.03E−11DENND2D 0.17 3.30E−12 0.01 0.631199717 FAIM3 0.17 3.53E−12 0.040.189542882 DDX3Y 0.17 4.25E−12 0.00 0.907797482 HLA-H 0.16 4.66E−120.21 1.54E−13 GPR56 0.16 5.64E−12 0.11 6.30E−05 MAF 0.16 5.82E−12 0.362.14E−38 TRIM69 0.16 7.34E−12 SEMA4A 0.16 9.52E−12 IL2RG 0.16 1.04E−110.18 6.51E−10 TMEM140 0.16 1.11E−11 0.09 0.00163736  GMDS 0.16 1.18E−110.08 0.008326449 LITAF 0.16 1.19E−11 −0.05 0.063294972 HSPA1A 0.161.56E−11 0.11 0.000172577 PAPOLA 0.16 1.56E−11 −0.01 0.70579933  AHI10.16 2.36E−11 0.16 9.85E−09 EZR 0.16 2.40E−11 0.14 1.92E−06 MIS18BP10.16 2.58E−11 0.17 6.15E−09 HLA-A 0.16 2.74E−11 0.32 9.78E−31 PSTPIP10.16 3.27E−11 0.11 9.40E−05 GBP5 0.16 3.71E−11 0.13 5.66E−06 RIN3 0.163.77E−11 HIF1A 0.16 3.97E−11 0.06 0.048813828 HLA-DRB6 0.16 4.67E−11PAG1 0.16 5.87E−11 −0.08 0.003384546 AKAP5 0.16 6.76E−11 KLRC3 0.166.90E−11 RFX5 0.16 8.25E−11 0.07 0.014179979 UBB 0.15 8.74E−11 0.135.13E−06 TXNDC11 0.15 9.85E−11 0.14 1.74E−06 FOXN2 0.15 1.00E−10 0.050.082411107 DUSP16 0.15 1.15E−10 0.13 1.07E−05 CD82 0.15 1.38E−10 0.181.30E−10 PELI1 0.15 1.40E−10 0.20 6.92E−13 AMIGO2 0.15 2.03E−10 CCDC1410.15 2.42E−10 0.06 0.036155173 TNIP3 0.15 2.63E−10 0.10 0.000563452 SAT10.15 2.71E−10 0.26 2.07E−20 LRBA 0.15 3.00E−10 0.12 2.66E−05 HLA-DMA0.15 3.36E−10 0.20 2.02E−12 MAPRE2 0.15 3.48E−10 0.10 0.000867905 BIRC30.15 3.71E−10 −0.01 0.720398325 EPSTI1 0.15 4.13E−10 0.18 5.86E−10 NCALD0.15 4.21E−10 0.22 5.12E−15 ID2 0.15 4.32E−10 −0.04 0.201480439 NFAT50.15 4.95E−10 0.14 5.55E−07 GOLIM4 0.15 6.33E−10 ZBTB32 0.15 6.70E−10NDUFB3 0.15 6.70E−10 0.13 3.74E−06 CALM3 0.15 7.24E−10 0.22 2.32E−14SHFM1 0.15 8.32E−10 0.09 0.000949937 HLA-DRB5 0.15 9.22E−10 0.171.46E−09 C21orf91 0.15 9.87E−10 0.07 0.011223721 CCND2 0.15 1.09E−090.02 0.530718461 BTLA 0.14 1.29E−09 0.16 1.30E−08 PRKCH 0.14 1.31E−090.12 3.11E−05 GALNT2 0.14 1.53E−09 IKZF3 0.14 1.77E−09 0.12 3.13E−05AMICA1 0.14 2.14E−09 −0.06 0.026070815 STAT1 0.14 2.64E−09 0.050.064028082 IRF8 0.14 2.81E−09 ELF1 0.14 2.91E−09 0.02 0.548742854 CD3D0.14 2.93E−09 0.16 5.77E−08 RBPJ 0.14 3.26E−09 0.12 2.32E−05 BATF 0.143.46E−09 0.34 3.15E−33 LRRC8D 0.14 3.57E−09 0.07 0.014705554 PMF1 0.143.60E−09 0.10 0.000379898 TNFSF4 0.14 4.01E−09

Example 4—Tumor Microenvironment Analysis in 26 Melanoma Tumors

T cells were also analyzed and the T cells contributed to thepredicative value of the signature of the present invention (FIG. 30).

The novel microenvironment cell-type signatures were very muchassociated with survival in both immunotherapy treated patients, and ingeneral. The genes which are up/down regulated in the immune cells afterimmunotherapy (CD4 T-cells, CD8 T-cells, B cells, and macrophages) areshown in Table 9.

TABLE 9 All Cell Type Signatures B cell Macrophage Malignant T cell cd4T cell cd8 ADAM19 AIF1 ACOT7 MFGE8 AIM1 APOBEC3G AKAP2 ALDH2 ACSL3 MFI2ANK3 CBLB BACH2 ANPEP ACTN1 MGST3 AQP3 CCL4 BANK1 C15orf48 ADAM15 MIACAMK4 CCL4L1 BCL11A C1orf162 ADI1 MIF CCR4 CCL4L2 BLK C1QA AEBP1 MITFCCR8 CCL5 CD19 C1QB AGPAT1 MLANA CD28 CD27 CD1C C1QC AGRN MLPH CD40LGCD8A CD22 C3AR1 AHCY MMP14 DGKA CD8B CD79A CCR1 AIF1L MORF4L2 EML4 CST7CD79B CD14 AKAP12 MORN2 FAAH2 CTSW CLEC17A CD163 AKT3 MPZL1 FBLN7 CXCL13CNR2 CD300A ANXA5 MRPL24 FKBP5 CXCR6 COL19A1 CD300C APOA1BP MT2A FLT3LGDTHD1 COL4A3 CD300LF APOD MTUS1 FOXP3 DUSP2 CPNE5 CD33 APOE MX11 FXYD5EOMES CR2 CD86 ARL2 MYH10 IL6R FASLG CXCR5 CFP ARNT2 MYO10 IL7R FCRL3EBF1 CLEC10A ARPC1A MYO1D ITGB2-AS1 GBP5 ELK2AP CLEC12A ASPH NAV2 JUNBGZMA FAM129C CLEC4A ATP1A1 NCKAP1 KLRB1 GZMB FAM177B CLEC5A ATP1B1 NDST1LEPROTL1 GZMH FCER2 CMKLR1 ATP6V0A1 NENF LOC100128420 GZMK FCRL1 CSF1RB3GNT1 NES MAL HCST FCRL2 CSF2RB BACE2 NGFRAP1 OXNAD1 HLA-A FCRL5 CSF3RBAIAP2 NGRN PBXIP1 HLA-B FCRLA CSTA BCAN NHSL1 PIK3IP1 HLA-H HLA-DOBCXCL9 BIRC7 NIDI PIM2 ID2 IGJ CXCR2P1 BTBD3 NME1 PRKCQ-AS1 IFNG IGLL1DSC2 C11orf24 NME2 RORA IL2RB IGLL3P FAM26F C17orf89 NME4 RPL35A KLRC3IGLL5 FBP1 C1orf198 NRP2 RPL4 KLRC4 KIAA0125 FCER1G C1orf21 NRSN2 RPL6KLRC4-KLRK1 KIAA0226L FCGR1A C1orf85 NSG1 RPS15A KLRD1 LOC283663 FCGR1BCALD1 OSBPL1A RPS27 KRK1 MS4A1 FCGR1C CALU P4HA2 RPS28 LAG3 P2RX5 FCGR3ACAPN3 PACSIN2 SEPT6 LSP1 PAX5 FCGR3B CAV1 PAX3 SLAMF1 LYST PNOC FCN1CBR1 PCDHGC3 SORL1 NKG7 POU2AF1 FOLR2 CCND1 PEG10 SPOCK2 PDCD1 POU2F2FPR1 CCT3 PFDN2 SUSD3 PRF1 RASGRP3 FPR2 CD151 PFKM TCF7 PSTPIP1 SEL1L3FPR3 CD276 PFN2 TMEM66 PYHIN1 SNX29P1 GGTA1P CD59 PGRMC1 TNFRSF18RARRES3 ST6GAL1 GNA15 CD63 PHB TNFRSF25 SH2D1A STAP1 GPR84 CD9 PHLDB1TNFRSF4 SH2D2A SWAP70 HCK CDC42BPA PIR TNFSF8 TARP TCL1A HK3 CDC42EP4PKNOX2 TRABD2A TIG1T TMEM154 IGSF6 CDH19 PLEKHB1 TSC22D3 TNFRSF9 VPREB3IL1B CDK2 PLK2 TXK TOX IL1RN CDK2AP1 PLOD1 IL4I1 CECR7 PLOD3 ITGAMCELSR2 PLP1 KYNU CERCAM PLS3 LGALS2 CERS2 PLXNA1 LILRA1 CHCHD6 PLXNB3LILRA2 CHL1 PMEL LILRA3 CHPF PMP22 LILRA4 CLDN12 POLR2F LILRB2 CLIC4POLR2L LILRB4 CNIH4 PON2 LILRB5 CNN3 PPT2 LST1 CNP PRAME MAFB CNPY2PRDX4 MARCO COA3 PRDX6 MNDA COL16A1 PRKCDBP MRC1 COMT PROS1 MS4A4A CRIP2PRSS23 MS4A6A CRNDE PSMB5 MSR1 CRTAP PTGFRN NCF2 CRYAB PTGR1 OLR1 CSAG1PTK2 P2RY13 CSAG3 PTPLAD1 PILRA CSPG4 PTPRM PLAU CSRP1 PTPRS PLBD1CTDSPL PTRH2 PLXDC2 CTHRC1 PTTG1IP PRAM1 CTNNAL1 PYCR1 RAB20 CTNNB1 PYGBRAB31 CTSF PYGL RASSF4 CTSK QDPR RBM47 CTTN QPCT RGS18 CYB5R1 RAB13S100A8 CYP27A1 RAB17 S100A9 CYSTM1 RAB34 SECTM1 CYTH3 RAB38 SIGLEC1DAAM2 RA114 SIGLEC7 DCBLD2 RBFOX2 SIGLEC9 DCT RCAN1 SLAMF8 DDR1 RCN1SLC31A2 DDR2 RCN2 SLC43A2 DIP2C RDX SLC7A7 DLC1 RGS20 SLC8A1 DNAH14 RND3SLCO2B1 DOCK7 ROBO1 SPI1 DST ROPN1 STAB1 DSTN ROPN1B TBXAS1 DUSP6 RTKNTFEC ECM1 S100A1 TGFBI EDNRB S100A13 TLR2 EFNA5 S100A16 TLR4 EIF4EBP1S100B TLR8 EMP1 SCARB1 TMEM176A ENTPD6 SCCPDH TMEM176B EPS8 SCD TNFSF13ERBB3 SDC3 TNFSF13B ETV4 SOC4 TREM2 ETV5 SDCBP TYROBP EVA1A SELENBP1VSIG4 EXOSC4 SEMA3B ZNF385A FAM127A SEMA3C FAM127B SEMA6A FAM167B SEPT10FARP1 SERPINA3 FARP2 SERPINE2 FASN SERPINH1 FKBP10 SGCD FKBP4 SGCE FKBP9SHC1 FN1 SHC4 FNBP1L SLC19A2 FRMD6 SLC24A5 FSTL1 SLC25A13 FXYD3 SLC25A4G6PC3 SLC35B2 GALE SLC39A1 GCSH SLC39A6 GDF15 SLC45A2 GJB1 SLC6A15 GLI3SLC7A8 GNG12 SMARCA1 GOLM1 SNAI2 GPM6B SNCA GPR143 SNHG16 GPRC5B SNRPEGSTA4 SORT1 GSTP1 SOX10 GULP1 SOX13 GYG2 SOX4 H1F0 SPARC HIBADH SPRHMCN1 SPRY4 HMG20B SPTBN1 HOXB7 SRPX HOXC10 SSFA2 HSBP1 ST3GAL4 HSP90AB1ST5 HSPB1 ST6GALNAC2 HSPD1 STK32A HSPG2 STMN1 IFI27 STXBP1 IGF1R SYNGR1IGFBP7 TANC1 IGSF11 TBC1D16 1GSF3 TBC1D7 IGSF8 TCEAL4 IMPDH2 TEAD1ISYNA1 TENC1 ITFG3 TEX2 ITGA3 TFAP2A ITGB3 TIMP2 KIRREL TIMP3 LAMB1 TJP1LAMB2 TMEM147 LAMC1 TMEM14C LAPTM4A TMEM9 LAPTM4B TMEM98 LDLRAD3TNFRSF19 LGALS1 TOM1L1 LGALS3BP TRIM2 LINC00473 TRIM63 LINC00673 TSC22D1LMNA TSPAN3 LOC100126784 TSPAN4 LOC100130370 TSPAN6 LOC645166 TTLL4LOXL4 TUBB2A LRP6 TUBB2B MAGEA12 TUBB3 MAGEA2B TYR MAGEA3 UBL3 MAGEA6VAT1 MAGED1 VIM MAGED2 VKORC1 MAP1B WASL MARCKSL1 WBP5 MDK WIPI1 MFAP2WLS XAGE1A XAGE1B XAGE1C XAGE1D XAGE1E XYLB YWHAE ZNF462

TABLE 10 Down-regulated and Up-regulated genes post-immunotherapytreatment in microenvironment T.cd8.up T.cd8.down T.cd4.up T.cd4.downMacro.up Macro.down AARS2 LYRM7 ACTN4 MAL AARS2 ACTR2 APOC1 AREG ABHD15MAP3K13 ADAM10 MAP1LC3A ABI2 ADRBK1 APOE ARF1 ABI2 MAP7D3 AEN MED21APOBEC3A ANAPC11 C17orf76- BRE-AS1 AS1 AK3 MAPK13 AIM1 MGMT APOL2ANKRD36BP1 C1orf56 CD55 AKAP5 MBOAT1 AIP MKNK2 ARF6 ARAP2 CA2 CREM AKIP1ME2 AKAP13 MPG C17orf76- ASCC3 CD81 DUSP2 AS1 ALG1 MED18 AKNA MRPL47C1orf56 ASMTL CSTB EREG ANKRD40 METTL16 AMD1 MRPL53 C1QB ATXN2L CXCL9ETS2 AP1G2 METTL2B ANKRD11 MRPL54 CASP10 BCL6 DBNDD2 FKBP5 AP3M1 MFSD11ANKRD36BP1 MSI2 CCL5 C22orf34 DHRS4L2 FOSB AP3S2 MIAT APBB1IP MT2A CCND2CALM3 DNAJC5B GAPT APOL2 MLANA APH1A MXD4 CD68 CCNG1 DYNC1I2 HIF1A ARF6MMS22L APOBEC3G MYCBP2 CEP41 CD200 DYNLL1 ICAM3 ARIH2OS MOCS3 ARID1AMYEOV2 CLUAP1 CD226 FABP3 IFI44L ARMC10 MREG ARID2 MYH9 CNNM3 CD3E FOLR2IL1B ARSA MRPL44 ARL1 NACA CTBP1 CD40LG FTL LOC100130476 ASB8 MS4A1ARL4C NAP1L1 CXCR3 CD58 FUCA1 MEF2C ATP6V0A2 MSH3 ASF1B NDC80 CXCR6 CD6GPNMB NFIL3 B2M MTFMT ATAD1 NDE1 DCAF10 CDC42EP3 HLA-J NFKBIA BCL6 NAA16ATP5E NDUFA12 DNAJC14 CHI3L2 HSD11B1 NFKBIZ BLOC1S6 NDNL2 ATP5L NDUFA13FAM126B COX7C HSD3B7 NLRP3 BMS1P1 NEK2 ATP5O NDUFA2 FAM134A CPSF1 HSPA7NR4A2 BMS1P4 NFKBIB ATP6V0C NDUFA4 FAM153C CTSA HSPB1 PPP1R15B BMS1P5NME6 ATP6V0E2 NDUFA6 FGD5-AS1 CXCR5 KLHDC8B REL BRIP1 NOL9 ATXN2L NFATC2GBP4 DDX39B MGLL RPSAP58 C10orf32 NPIPL3 ATXN7L1 NFKBIA GBP5 DDX3YMIR4461 THBS1 C12orf65 NQO1 AURKB NFKB1Z GNRHR2 DHRS7 MRPS15 TNFAIP3C19orf40 NT5DC3 BCL11B N1NJ2 GPR56 EHD1 NOP10 ZBTB16 C1orf174 NUAK2 BCL2NIPBL GSTM3 EIF3L NUPR1 ZFP36 C1orf210 OCLN BHLHE40- NIT2 GZMA ERGIC3PCBD1 AS1 C1orf56 OPHN1 BIRC5 NOP56 HAUS2 EXOC1 PLA2G2D C1orf63 ORC6BLMH NPM1 HERC2P4 FAM172A PLA2G7 C1QTNF6 PACS2 BLVRA ORMDL3 HLA-DRA GNG5RAB20 C5orf24 PAFAH1B2 BRK1 OST4 HLA-DRB1 GPRIN3 SCARB1 C5orf33 PAICSBTF3 PABPC1 HNRNPH1 HDDC2 SLIRP C9orf3 PAN3 BTN3A2 PAIP2 INADL HINT1ST3GAL5 C9orf85 PAR-SN BUB1 PAM KLRD1 HIST1H1D TIMP2 CACUL1 PARP11 BUB3PARK7 LINC00439 HIST1H1E TMSB10 CAMLG PARP3 C1D PARP8 LOC100506469HIVEP2 TRNAU1AP CCDC122 PARP9 CARD16 PCBP1 LOC284379 HNRNPC UBD CCR6PCGF5 CARS PCBP2 LOC389641 HS3ST3B1 WSB2 CD160 PDE12 CASP4 PDCD1LOC644961 ICA1 XIST CD24 PER2 CASP8 PDCD5 LOC727896 ITM2A YTHDF2 CD68PEX13 CBLB PER1 MAP3K13 ITPR1 CENPN PIGX CCDC141 PET117 MCTS1 KLF12CEP104 PKNOX1 CCDC167 PFDN5 NANOG LCMT1 CHP1 PMEL CCDC23 PIK3IP1 NXNL2LOC100216545 CLCC1 POU2AF1 CCL4 PIK3R5 PIP4K2A LOC100271836 CLUAP1PPP1R3B CCNB2 PIN4 PLEKHA2 LOC285740 CNNM3 PQLC2 CCND1 PLCB2 PPID MAEACOA1 PRMT2 CCND3 PLEK PRDM1 MAP2K3 COX10-AS1 PSTPIP2 CCNH PLEKHM1PSTPIP2 MAP4K1 COX18 PTPN2 CCNK PLIN2 QRSL1 MED21 CPPED1 QPRT CCR1 POGZRASSF3 MKNK2 CPT1A RAB21 CCR4 PPIA RBM43 MRPL33 CRK RAB33B CCR5 PPM1GRGS1 MRPS2 CSAD RAD1 CD2 PRDM1 RPP14 MTERFD2 CSNK1G1 RASSF1 CD200R1PRDX6 RUNX1-IT1 MTMR6 CWC25 RBBP5 CD27 PRMT10 SBF2-AS1 MYEOV2 CYB5D2RBL1 CD320 PRPF8 SCAI NAB1 CYP4V2 RBMS2 CD37 PRR14L SGOL1 NDUFA4 DCP1ARDH10 CD3D PRRC2B SLC25A51 NEK7 DESI1 REL CD3E PTBP3 SLC35E1 NFATC1 DGKDRFC2 CD3G PTPN4 SPDYE1 NFATC2 DHODH RFT1 CD4 PTRHD1 SPDYE2 NINJ2 DIP2ARHD CD7 RAB1B SPDYE2L OST4 DIS3 RIOK3 CD79A RAPGEF1 SPDYE7P P2RX5 DIS3LRNF14 CD81 RASA1 SWSAP1 PAPD4 DNASE1 RNF141 CDC42SE1 RASA2 THAP5 PARLDND1 RPS6KA3 CDK1 RBM38 TMEM120B PASK DTD2 RUNDC1 CENPK RGS1 TMEM192PCBP1 EEF2K S1PR2 CHCHD2 RGS10 TP53RK PDCD1 EIF5A2 SATB1 CHI3L2 RHBDD3TRMT10B PFKL ELMSAN1 SCAI CIRBP RHOA TSNAX PHF3 ESYT2 SCAMP1 CITED2RNASEK TXNDC15 PHF8 EYA3 SCML4 CLASP1 RPA3 UGT8 PIK3CG F11R SEC23IPCLDND1 RPL13A UPK3BL PLP2 FAM126B SEMA4D CLECL1 RPL14 XIST PON2 FAM210BSENP5 COX17 RPL18 ZNF253 PPP1CA FAM215A SERPINB1 COX4I1 RPL22 ZNF276PRKCH FAM217B SERPINB6 COX6A1 RPL23 ZSWIM7 PRNP FAM73A SGCB COX7A2LRPL27 PRRC2B FANCD2 SGK3 COX7C RPL27A PTBP3 FASTKD2 SGOL1 COX8A RPL29RBM25 FBLIM1 SH2D1B CREB3L2 RPL31 RERE FBXL18 SIRT5 CSNK1D RPL32 RGS3FBXW2 SKP2 CST7 RPL34 RPL13A FCRL3 SLAMF7 CTSC RPL35 RPL14 FCRL6SLC25A15 CTSD RPL35A RPL27 FDPSL2A SLC25A32 CXCL13 RPL36 RPL37 FLCNSLC25A51 CXCR4 RPL36A RPS26 FLOT1 SLC2A3 CXCR6 RPL36AL RPS4Y1 FOXK1SLC30A6 CYTIP RPL37 RPS5 FTO SLC30A7 DDIT4 RPL37A SARDH FXN SLC31A1 DDX6RPL38 SEC11C GALNT6 SLC35A3 DNAJB12 RPL39 SEC16A GATAD1 SLC48A1 DNAJC9RPLP0 SELT GBP1 SLC50A1 DPM3 RPRD2 SF3B1 GBP2 SLC7A5P2 DTHD1 RPS10 SFI1GBP4 SMIM14 DUSP4 RPS13 SMARCE1 GBP5 SMYD4 EBP RPS16 SMG1P1 GCLM SNAPC3EEF1B2 RPS17 SNHG5 GDAP2 SNHG7 EEF1D RPS17L SNRPN GEMIN8 SNIP1 EEF2RPS20 SRRM2 GGPS1 SOAT1 EHMT1 RPS21 SSH2 GLIPR1L2 SPAST EIF3F RPS23STAU1 GLUD1P7 SPRYD4 EIF3G RPS24 TATDN1 GMEB1 SRSF8 EIF4B RPS26 TCF7 GNESS18 ELK2AP RPS28 THADA GNG4 STAT1 EMB RPS29 TIAM1 GNRHR2 STAT5B ENSARPS4X TIGIT GOLGA3 STOM ERAP2 RPS5 TMEM59 GPCPD1 STYX ERGIC3 RPS7 TOXGPR82 SWSAP1 ERH RPS9 TOX2 GTF2H2C TADA2B ERN1 RPSA TYK2 GTPBP5 TADA3ETS1 RSBN1 UBQLN1 HAUS3 TANGO2 EVL RUNX2 UQCR10 HERC2P7 TARS2 FAM102ARUNX3 UQCRH HIST1H2BG TATDN3 FAM129A S100A6 UTRN HIVEP3 TBC1D24 FAM53BSELL UXT HMHA1 TBCCD1 FAM78A SF3A1 WNK1 HOGA1 TERF1 FAU SHISA9 WWP2 HOPXTERF2 FBXO5 SIRPG ZFP36 HSPA1B THAP5 FKBP5 SLA ZNF217 ICA1L TLE3 FNDC3ASLC39A7 ID3 TM7SF3 FOSB SLC4A7 IDO1 TMEM123 FOXP1 SMG7 IER2 TMEM209 FRYLSNORD10 IFITM3 TMEM41A G6PD SNRNP200 IFNAR1 TMEM41B GAS5 SON IFNLR1TNFA1P8L2- GINS2 SPOCK2 SCNM1 IKBIP TNFSF14 GLRX SRRM2 IL10 TPMT GMCL1SSR4 INIP TRIM5 GMFG STK16 INPP4B TRIOBP GMNN SUMO2 INPP5F TSNAX GNG5SUPV3L1 IRAK4 TTC39C GNLY SYNGR2 IRF1 TUBGCP4 GOLGA8B SYTL3 IRF2BP2 TYMPGPR183 TAF15 ITGAX UBE2Q2 GPR56 TAOK3 ITK UBOX5 CRN TAP2 KCNK5 UBXN2BGSTM1 TK1 KDELC2 UTP23 GSTP1 TLN1 KDSR VMP1 GTF2B TMED9 KIAA0355 WAC-AS1GTF3C6 TMEM155 KIAA1324 WDR92 GZMK TMEM2 KIAA1919 XIAP H2AFZ TNFAIP3KIF18B XKR9 HDAC8 TNFSF4 KIF3A ZBTB24 HERC2P2 TNFSF8 KIN ZBTB43 HERPUD1TOB1 KLHL28 ZCCHC4 HINT1 TOMM7 KLRC2 ZFP14 HIST1H1E TOX KLRC3 ZFP36L1HIST1H3G TP53INP1 KLRD1 ZMYM5 HIST1H4C TPX2 KRAS ZNF100 HLA-DQA1 TSC22D3LAIR1 ZNF124 HLA-DQA2 TSPAN14 LDHA ZNF16O HLA-DRB5 TSPYL2 LDLR ZNF321PHLA-F TSTD1 LIAS ZNF333 HLA-H TTN LINC00476 ZNF37BP HMBOX1 TUBA4A LLGL1ZNF483 HUWE1 TXK LOC100131067 ZNF526 IFIT5 TXNIP LOC100131089 ZNF528IL6ST TYMS LOC180132247 ZNF529 IQGAP1 UBA52 LOC100190986 ZNF543 IQGAP2UBE2C LOC100268168 ZNF548 ISCU UBE2T LOC100271836 ZNF549 ISG20 UCP2LOC10050812 ZNF620 ITGAD UGDH- AS1 LOC100505876 ZNF652 ITGB1 UQCR11LOC100506083 ZNF665 ITGB2 UQCRB LOC100652772 ZNF669 ITM2B UQCRHLOC202781 ZNF683 KDM5C USB1 LOC284023 ZNF721 KIAA1551 UXT LOC389641ZNF793 KIR2DL4 VCAM1 LOC727896 ZNF805 KLF12 VRK1 LOC729603 ZNF814 KPNB1WDR83OS LOC90834 ZSCAN2 LDHB WNK1 LRRC57 ZSCAN22 LENG8 YEATS4 LRRC58ZSCAN29 LINC00493 YWHAB LY9 ZSWIM7 LINC00612 ZBTB38 LNPEP ZC3H12ALOC643406 ZC3HC1 LOC643733 ZDHHC24 LOC646214 ZFP36L2 LRRC37A4P ZMYND8LSM6 ZNF638 MAD2L1 ZWINT MAEA

TABLE 11 Top Genes from Table 10 T.cd8.up T.cd8.down T.cd4.up T.cd4.downMacro.up Macro.down AP1G2 AKNA CASP10 CHI3L2 NUPR1 FKBP5 AP3M1 BCL2CXCR3 COX7C LOC100130476 APOL2 CARD16 CXCR6 CXCR5 NLRP3 ARF6 CCDC141FAM153C HIST1H1E THBS1 C12orf65 COX4I1 FGD5-AS1 HIVEP2 TNFAIP3 CCDC122COX8A GBP5 ICA1 CSAD EIF3G LOC727896 NEK7 CWC25 FAU NXNL2 NFATC2 DHODHG6PD RBM43 NINJ2 DIS3L GLRX RGS1 PASK FAM217B GNLY SLC35E1 RPL13A GBP2GPR56 SPDYE1 TCF7 GDAP2 HIST1H4C HOPX HLA-DRB5 IKBIP HUWE1 KIAA1919ITGB2 LOC727896 MGMT LOC90834 MKNK2 LRRC58 NDC80 MAP7D3 NDUFA6 MFSD11PIK3R5 MOCS3 RPL35A PER2 SYTL3 POU2AF1 TNFSF4 PQLC2 TOB1 RAD1 UCP2 SGCBWNK1 SGOL1 SLC2A3 SNAPC3 SRSF8 SS18 STOM SWSAP1 TANGO2 TERF2 TMEM123TMEM209 ZBTB43 ZNF160 ZNF528 ZNF543

Example 5 Protein-Protein Interactions Between Genes in the ResistanceSignatures

In line with the co/anti-regulatory patterns of the PIT-Up (ICR-Up) andPIT-Down (ICR-down) modules, a significantly large number ofprotein-protein interactions occur within and between the two modules(253 interactions, P=<1e-3,) (Table 12). The number of interactions is˜7 times more than expected (empirical p-value)

TABLE 12 GeneA GeneB ACAA2 PFN1 ACAA2 ATP1B3 ACAA2 ISYNA1 ACSL4 PTPMT1ACSL4 HTATIP2 ADSL UBC ADSL XPNPEP1 ADSL PAICS AEN LZTS2 AHNAK FN1 AHNAKS100A10 ALDH1B1 UBC ALDH1B1 FN1 ALDH1B1 XPNPEP1 ANXA1 UCHL5 ANXA1 FN1ANXA2 CTSB ANXA2 S100A10 ANXA2 MID1 ANXA2 FN1 ANXA2 LGALS1 ARHGEF1 CD44ARHGEF1 FN1 ATF3 STAT1 ATF3 JUNB ATP1A1 UBC ATP1A1 ATP1B3 ATP1B3 PTP4A3ATP1B3 HLA-C ATP1B3 RPL17 ATXN10 BSG ATXN10 FN1 ATXN10 MRPS16 ATXN2LGALNS ATXN2L PABPC1 BCCIP EIF6 BCCIP FAM46A BCCIP SORD BCCIP SMS BCL6JUNB BCL6 HDAC2 BCL6 PELI1 BIRC3 UBC BSG OS9 BSG MYBBP1A BSG XPO7 BSGMETAP2 BSG PTPMT1 CALU GAA CALU PRKCDBP CALU CTNNAL1 CALU HSP90B1 CAV1CD44 CAV1 PTRF CD151 CD46 CD44 IGFBP3 CD44 FN1 CD44 NF2 CD46 CD9 CD9LGALS3BP CFB FN1 CPSF1 POLR2A CRELD1 EIF6 CRYAB CS CRYAB SORD CS CTPS1CST3 CTSB CTSA CTSD CTSB S100A10 CTSB SPRY2 CTSD UCHL5 CTSD HSP90B1DCBLD2 ITM2B DCTN6 RPSA ECHS1 UCHL5 ECHS1 ISYNA1 EGR1 JUNB EIF4A1 PABPC1EIF4A1 UCHL5 EIF4A1 RPSA EIF4A1 TMEM43 EIF4A1 ILF2 EIF4A1 FN1 EIF6 PAICSEIF6 PSME1 EIF6 FBL EIF6 RPL17 EIF6 RUVBL2 EIF6 TSNAX EIF6 KIAA0020 EMP1SMIM3 EPDR1 NF2 FAM213A HLA-C FAM46A PRSS23 FAM46A SQRDL FAM46A FNDC3BFBL RUVBL2 FBL KLF6 FBL UBC FBL NOLC1 FBL RPL17 FBL RPS7 FBL RPS3 FBLFN1 FBL GPATCH4 FBL KIAA0020 FBLN1 FN1 FN1 IGFBP3 FN1 MIA FN1 TNC FN1LGALS1 FN1 LYPLA1 FN1 RPL17 FN1 RNH1 FN1 G6PD FN1 PAICS FN1 SLC5A3 FN1NCBP1 FN1 PPA1 FN1 XRCC5 FN1 RPSA FN1 RUVBL2 FN1 PRDX3 FN1 RPL10A FN1RPS7 FN1 ILF2 FN1 PFN1 FN1 UBAP2L FN1 PABPC1 FN1 RPS3 FN1 UBC FN1 RBM4FN1 TF FOXRED2 OS9 FXYD3 NR4A1 G6PD GBP2 G6PD TSTA3 G6PD IDH2 GEM LZTS2GLOD4 NR4A1 GLOD4 NNMT GLOD4 PAICS HDAC2 SMC3 HDAC2 KLF4 HDAC2 RUVBL2HDAC2 SNAI2 HDAC2 TSC22D3 HLA-A TAPBP HLA-A TAP1 HLA-A UBC HLA-A ITM2BHLA-A HLA-C HLA-A HLA-E HLA-C UBC HLA-C HLA-F HLA-C HLA-E HLA-C ITGA6HLA-E HLA-F HLA-E ITGA6 HSP90B1 OS9 HSP90B1 TPM1 HSP90B1 RPN2 HSP90B1TSR1 HSP90B1 STAT1 IDH2 UBC IGF1R IGFBP3 IGFBP3 TF ILF2 XRCC5 ILF2 RPL17ILF2 RPL10A ILF2 RPS3 ILF2 SRSF7 ILF2 PRKCDBP ILF2 TOMM22 ILF2 PTRF ILF2RUVBL2 ILF2 MYBBP1A ILF2 KIAA0020 ITGA6 LGALS3BP KLF4 KLF6 LAMB1 UBCLGALS1 LGALS3BP LZTS2 TSNAX LZTS2 SMIM3 MID1 RPS3 MID1 UBC MTG1 PRNPMYBBP1A NR4A1 MYBBP1A RPS3 MYBBP1A PTRF NCBP1 THOC5 NCBP1 SERPINE2 NF2XRCC5 NF2 RPS3 NF2 RPS7 NF2 SMC3 NOLC1 PTRF OXA1L PTPMT1 PABPC1 RPSAPABPC1 RBM4 PABPC1 RPL10A PABPC1 RPL17 PELI1 UBC PFN1 UCHL5 POLR2A XRCC5POLR2A SMC3 POLR2A PSMB9 POLR2A RUVBL2 PRAME UBC PRDX3 UCHL5 PRDX3 PSME1PROS1 RPSA PSMB9 PSME1 PSMB9 UCHL5 PTP4A3 XPO7 RND3 SKP2 RPL10A RPS3RPL10A RPL17 RPL10A RPSA RPL10A S100A10 RPL10A RPS7 RPL17 RPS3 RPL17RPSA RPN2 UBC RPS3 RPS7 RPS3 RPSA RPS3 TPM1 RPS3 TSR1 RPS3 UBC RPS3TSNAX RPS7 RPSA RPS7 TSR1 RPSA TSR1 RUVBL2 SRCAP RUVBL2 UCHL5 RUVBL2 UBCRUVBL2 VPS72 SAMM50 TOMM22 SAMM50 SQRDL SAMM50 SERINC1 SMG7 TSNAX SMSSORD SORD TPM1 SRCAP VPS72 STAT1 TSNAX TAPI TAPBP TPM1 UBC TSC22D3 UBCTSTA3 UBC UBA7 UBE2L6 UBC UCHL5 UBC XPNPEP1 UCHL5 XPNPEP1

Example 6—Tumor Microenvironment Interaction Analysis

The ITR-down genes and ITR-up genes interact with stromal and immunegenes. The ITR-down genes interact with more genes (FIG. 34, 35). FIG.33 shows that genes that are down in malignant cells in immunotherapyresistant samples are rich in interactors of immune and stromal cells.Conversely, few such interaction genes are induced in malignant cells inimmunotherapy resistant samples.

Example 7—ITR Signature Scores from 26 Melanoma Tumors in DifferentCancers

The ITR scores are different in different cancers (FIG. 36, 37). Bladdercancer has the highest. Thymoma has the lowest. Uveal melanoma has thefourth highest. Applicants observed a difference in score between twomelanomas (uveal and skin cutaneous). Not being bound by a theory,cancers with the highest ITR scores are more resistant to immunotherapythan cancers with a lower score. Not being bound by a theory, cancerswith the highest ITR scores have a worse prognosis. The cancers on theright are more sensitive to immunotherapy (FIG. 36). Furthermore, theyhave less of an anti-correlation between ITR and T cell infiltration.

Example 8—Analysis of Single Cells from ER+ Metastatic Breast Cancer andColon Cancer

Applicants also analyzed single cells in other cancers having an ICRsignature, (see, e.g., FIG. 15A, B). Applicants further extended themelanoma ecosystem studies to study response to immunotherapy, usingmassively parallel droplet scRNA-Seq to analyze cells from colon tumors,using snRNA-Seq methods to profile metastatic breast cancer samples andprofiling pancreatic tumors. Cancer cells may be more or less resistantto immunotherapy based on uICR scores. Single cells in other cancers maybe shifted to an immunotherapy sensitive signature by treating withCDK4/6 inhibitors. Analysis of this signature and measuring shifts inthe signature after CDK4/6 inhibition can allow the properadministration of an immunotherapy in a combination treatment.

Applicants analyzed ER+ metastatic breast cancer using single nucleiRNA-seq (snRNA-seq) on fresh and frozen tissue samples (FIG. 38).snRNA-seq as described herein is compatible with frozen tissue samples.Non-malignant cells clustered by cell type in both frozen and freshtissue samples. Malignant cells clustered by patient.

Applicants analyzed 22 colon cancer samples using scRNA-seq (FIG. 39).With strict quality control (QC) on the 22 samples analyzed Applicantsobtained 12,215 epithelial cells and 17,143 non-epithelial cells.

Example 9—Immunotherapy Resistance Signature

Immunotherapies have transformed the therapeutic landscape of severalcancer types (Sharma and Allison, 2015). However, despite the durableresponses in some patients, most patients' tumors manifest unpredictableresistance to immunotherapies (Gibney et al., 2016; Sharma et al.,2017). This hampers appropriate selection of patients for therapies,rational enrollment to clinical trials and the development of newtherapeutic strategies that could overcome resistance (Sharma andAllison, 2015). Most non-responding patients manifest intrinsicresistance, reflected as continued tumor growth or occurrence of newmetastatic lesions despite therapy, whereas some patients developacquired resistance following an initial clinical disease regression. Itis unknown whether these clinically discrete manifestations areassociated with shared or distinct molecular mechanisms of resistance(Sharma et al., 2017).

Recent studies characterized resistance to immune checkpoint inhibitors(ICI) by analyzing Whole Exome Sequencing (WES) and transcriptionalprofiles of bulk tumors (Hugo et al., 2016; Mariathasan et al., 2018;Van Allen et al., 2015). These studies demonstrated that tumors with ahigh mutational load (Van Allen et al., 2015) and a high level of immunecell infiltration (Riaz et al., 2017; Tumeh et al., 2014) are morelikely to respond, and linked ICI resistance in patients to functionalimmune evasion phenotypes, including defects in the JAK/STAT pathway(Zaretsky et al., 2016) and interferon gamma (IFN-γ) response (Gao etal., 2016; Zaretsky et al., 2016), impaired antigen presentation (Hugoet al., 2016; Zaretsky et al., 2016), and PTEN loss (Peng et al., 2016).While these studies significantly contributed to the understanding ofthe cancer-immune interplay, the resulting biomarkers where onlypartially predictive (Sharma et al., 2017). This may be due to the factthat they only reflect some facets of the causes of resistance (WES) orcombine signals from malignant and non-malignant (immune and stroma)cells (RNA and copy-number variations).

Because immune checkpoint inhibitors target the interactions betweendifferent cells in the tumor, their impact depends on multicellularcircuits between malignant and non-malignant cells (Tirosh et al.,2016a). In principle, resistance can stem from different compartment ofthe tumor's ecosystem, for example, the proportion of different celltypes (e.g., T cells, macrophages, fibroblasts), the intrinsic state ofeach cell (e.g., memory or dysfunctional T cell), and the impact of onecell on the proportions and states of other cells in the tumor (e.g.,malignant cells inducing T cell dysfunction by expressing PD-L1 orpromoting T cell memory formation by presenting neoantigens). Thesedifferent facets are inter-connected through the cellular ecosystem:intrinsic cellular states control the expression of secreted factors andcell surface receptors that in turn affect the presence and state ofother cells, and vice versa. In particular, brisk tumor infiltrationwith T cell has been associated with patient survival and improvedimmunotherapy responses (Fridman et al., 2012), but the determinantsthat dictate if a tumor will have high (“hot”) or low (“cold”) levels ofT cell infiltration are only partially understood. Among multiplefactors, malignant cells may play an important role in determining thisphenotype (Spranger et al., 2015). Resolving this relationship with bulkgenomics approaches has been challenging; single-cell RNA-seq(scRNA-seq) of tumors (Li et al., 2017; Patel et al., 2014; Tirosh etal., 2016a, 2016b; Venteicher et al., 2017) has the potential to shedlight on a wide range of immune evasion mechanisms and immunesuppression programs.

Here, Applicants used scRNA-seq and a new computational approach toidentify immune evasion or suppression mechanisms in the melanomaecosystem (FIG. 44A,B). Applicants developed a data-driven approach thatintegrates scRNA-seq with other data sources to characterize malignantcell states that drive immune resistance in melanoma (FIG. 44B).Applicants identified a program in malignant cells that is associatedwith T cell exclusion prior to immunotherapy, and with the melanoma cellstates in patients who were resistant to immunotherapies. Applicantsconfirmed its presence in situ in tumors with multiplex protein imaging.This program predominantly reflects intrinsic resistance to immunecheckpoint inhibitors (but not to RAF/MEK-targeted therapy) and itsexpression predicts responses to ICI and clinical outcomes inindependent patient cohorts. Applicants further associated the CDK4/6pathway with control of this program and showed that treatment withCDK4/6 inhibitors reverses it and promotes a senescent-like state. Thiswork provides a new predictive biomarker for ICI response, suggests anew therapeutic modality that may re-sensitize malignant melanoma cellsto ICI, and provides a general framework to study the effect ofimmunotherapies and other drugs on complex tumor ecosystems.

Results

Systematic Approach to Discover Malignant Cell Programs Associated withImmune Cell Infiltration or Exclusion

To identify malignant cell programs that characterize “cold” melanomatumors, Applicants devised a new strategy that combines scRNA-seq andbulk RNA-Seq data to relate the cellular state of one cell type (e.g.,malignant cell states) to the cellular composition of the tumors (e.g.,T cell infiltration vs. exclusion) (FIG. 44B). For clarity, Applicantsdescribe the strategy in this specific context, though it can be appliedto any two cell-types of interest. Applicants first use scRNA-seqprofiles to define cell type specific signatures of T cells and ofmalignant cells in melanoma tumors. Next, Applicants use the T cellsignature to estimate T cell infiltration levels in each of hundreds oftumors, based on their bulk RNA-Seq profile. Applicants then define a“seed exclusion program” by identifying genes from the malignant cellsignature whose expression is strongly correlated (positively ornegatively) with the T cell infiltration level across those bulk tumors.Because the seed program is identified only among a few hundred genesthat are exclusively expressed by scRNA-Seq in malignant cells, itavoids contamination from the tumor microenvironment; however, importantgenes that promote exclusion or infiltration may also be expressed bynon-malignant cells (e.g., MHC class I molecules). To recover thesegenes, Applicants finally return to the scRNA-seq data of the malignantcells and expand the seed program by searching for genes that arecorrelated with it across the single malignant cells, irrespective oftheir expression in other cell types. In this way, Applicants derive agenome-scale, malignant-cell exclusion program, consisting of genesinduced (“up”) or repressed (“down”) by malignant cells in “cold” vs.“hot” tumors. Applicants can then score each cell or tumor forexpression of the program, such that overexpression of the program isdefined as the overexpression of its induced part and underexpression ofits repressed part, and vice versa (Methods).

Analysis of Clinical scRNA-Seq Identifies a Malignant Cell ProgramAssociated with T Cell Exclusion from Melanoma Tumors

Applicants applied the approach to 7,186 high-quality scRNA-seq profilesfrom the tumors of 31 melanoma patients, comprised of 2,987 cells from16 newly collected patient tumors (FIG. 44A, Table S1—note that only inthis example (9) cohort 1 is referred to as cohort 2 and cohort 2 isreferred to as cohort 1), and 4,199 cells from 16 patients thatApplicants previously reported (Tirosh et al., 2016a), along with 473bulk RNA-seq melanoma profiles from The Cancer Genome Atlas (TCGA)(Akbani et al., 2015). Applicants dissociated individual cells fromfresh tumor resections, isolated immune and non-immune cells by FACSbased on CD45 staining, and profiled them with a modified full-lengthSMART-Seq2 protocol (Methods, Table S2). Applicants distinguisheddifferent cell subsets and genetic clones both by their expressionprofiles and by their inferred CNV profiles (Tirosh et al., 2016a)(Methods), identifying: malignant cells, CD8 and CD4 T cells, B cells,NK cells, macrophages, Cancer Associated Fibroblasts (CAFs) andendothelial cells (FIGS. 44C,D and 51, Tables S3 and S4). Overall,malignant cells primarily grouped by their tumor of origin (FIG. 44C),while the non-malignant cells grouped primarily by their cell type, andonly then by their tumor of origin (FIG. 44D), as Applicants havepreviously reported for melanoma and other tumor types (Puram et al.,2017; Tirosh et al., 2016a; Venteicher et al., 2017).

The resulting exclusion program (FIG. 44E, Table S6) highlights therepression of diverse immune response pathways and the induction of aco-regulated gene module of Myc and CDK targets. The repressed geneswere enriched for antigen processing and presentation genes (B2M, CTSB,CTSL1, HLA-B/C/F, HSPA1A, HSPA1B, P=4.19*10⁻⁷, hypergeometric test),immune modulation genes (P=3.84*10⁻⁹, e.g., CD58 and the NFκB inhibitor,NFKBIA), and genes involved in the response to the complement system(P=2.26*10⁻⁷, e.g., CD59 and C4A). CD58 KO in malignant cells wasrecently shown to enhance the survival of melanoma cells in agenome-scale CRISPR screen of melanoma/T cell co-cultures (Patel et al.,2017), and its genetic loss or epigenetic inactivation are frequentimmune evasion drivers in diffuse large B cell lymphoma (Challa-Malladiet al., 2011). The induced genes included MYC and Myc targets(P=2.8*10⁻¹⁴), many CDK7/8 targets (P<3*10⁻⁹) (Oki et al., 2018), andtranscription factors, such as SNAI2 and SOX4. Myc-activation has beenpreviously linked to increased expression of immunosuppressive signals,including the upregulation of PD-L1 and β-catenin, which in turninhibits dendritic cell recruitment to the tumor microenvironment viaCCL4 (Spranger et al., 2015).

The Exclusion Program Characterizes Individual Malignant Cells fromPatients Who Failed Immunotherapy

To determine whether the malignant T cell exclusion program manifests inthe context of immune checkpoint inhibitor therapy, Applicants leveragedthe fact that the scRNA-seq cohort included both untreated patients andpost-ICI patients who manifested intrinsic resistance. As clinicalresponse rates to ICI vary, with up to 61% responders with combinationtherapies (Hodi et al., 2010; Larkin et al., 2015; Postow et al., 2015;Ribas et al., 2015), the untreated tumors Applicants profiled likelyinclude both ICI sensitive and ICI resistant tumors, whereas the tumorsfrom ICI resistant patients are expected to include primarily resistantmalignant cells. Applicants thus turned to examine if the exclusionprogram is more pronounced in the malignant cells from ICI resistant vs.untreated patients. ScRNA-seq data provide particular power for suchinter-patient comparisons, even when considering only a small number oftumors, because of the larger number of cells per tumor and becausenon-malignant cells in the tumor microenvironment do not confound theanalyses.

Applicants thus independently identified a post-treatmenttranscriptional program, consisting of features that distinguishindividual malignant cells from post-ICI resistant tumors compared tomalignant cells from untreated tumors (Table S6). Applicants found arobust post-treatment program, consisting of genes induced (up) andrepressed (down) by malignant cells from the post-treatment resistantvs. untreated patients, which is stable and generalizable incross-validation (Methods, FIG. 45A, AUC=0.83). In principle, theprogram might reflect both the overall impact of ICI therapy andintrinsic ICI resistance per se, but those cannot be directlydistinguished based on the single-cell cohort, where Applicants did nothave matched samples from the same patient or pre-treatment tumors fromresponders and non-responders. Applicants address this below byanalyzing two independent validation cohorts.

The post-treatment program substantially overlapped the exclusionprogram (FIGS. 44E and 45B,C, Table S6; P<10⁻¹⁶, hypergeometric test,Jaccard index=0.27 and 0.23, for induced and repressed genes,respectively) and highlighted similar modules and pathways (FIG. 45D),even though the exclusion program was identified without considering thetreatment status of the tumors in the scRNA-seq data and with bulkRNA-Seq data of untreated patients. Both programs robustly classifiedindividual cells as untreated or post-treatment (AUC=0.83 and 0.86 forcross-validation post-treatment and exclusion, respectively, FIG.45A,E). In light of this congruence, Applicants defined a unified immuneresistance program as the union of the corresponding post-treatment andexclusion programs, and used it in all subsequent analyses, unlessindicated otherwise.

The Immune Resistance Program Reflects a Coherent Multifaceted State ofImmune Evasion

The program is consistent with several hallmarks of active immuneevasion, suppression and exclusion. First, it is more pronounced inuveal melanoma, which resides in an immune-privileged environment andhas very low response rates to immunotherapy, compared to cutaneousmelanoma (FIG. 46A) (Algazi et al., 2016; Zimmer et al., 2015). Second,the inhibition of genes from the repressed component of the program inmalignant melanoma cells conferred resistance to CD8 T cells in agenome-wide CRISPR KO screen (P=6.37*10⁻³, hypergeometric test) (Patelet al., 2017). Third, malignant cells which express the programsubstantially repress a significant number of interaction routes withother cell types in the tumor microenvironment, including WIC I:TCR (Tcells), CD58:CD2 (T cells), and IL1RAP:IL1B (macrophages) (FIG. 46B,Methods), as well as the overall Senescence Associated SecretoryPhenotype (SASP) (P=4.3*10⁻¹⁶⁶ and 3.6*10⁻³, one-sided t-test and mixedeffects, respectively, FIG. 45D, right).

The program genes appear to be under shared control by one or a fewmaster regulators, with opposing effects on the repressed and inducedcomponents of the program. There was a strong positive correlationwithin the induced or repressed genes, and a strong anti-correlationbetween the induced and repressed genes, both across single cells in thesame tumor and across TCGA tumors (FIGS. 46C,D). The co-variationpatterns were remarkably reproducible within each one of the tumors inthe cohort (FIG. 52), such that any given aspect of the program (e.g.,under-expression of MHC-1 genes in a cell) is coupled to the state ofthe entire program. Moreover, there is a significant overlap between theperturbations that reverse the expression of the program's repressed andinduced components (p-value=2.33*10′, hypergeometric test), includingthe overexpression of IFN-γ and IFN-β and the knockdown of MYC(Subramanian et al., 2017). Indeed, MYC knockdown is among the topperturbation to repress the program, which is enriched for Myc targets.

Expression of Resistance Program Features in Malignant Cells in TCell-Depleted Niches In Situ

If the immune resistance program in malignant cells is associated with Tcell exclusion, malignant and T cells should vary in their relativespatial distribution in tumors depending on the activity of the program.To explore this, Applicants used multiplexed immunofluorescence(t-CyCIF) (Lin et al., 2017) to stain histological sections of 19 tumorsfrom the single-cell cohort for 14 proteins: six cell type markers (CD3,CD8, FOXP3, S100, and MITF) and eight members of the immune resistanceprogram (induced: p53, CEP170, Myc, DLL3; repressed: HLA-A, c-Jun,SQSTM1, LAMP2). Following cell segmentation and estimation of antibodystaining intensities (Methods), Applicants assigned cells (424,000cells/image on average) into malignant cells (S100⁺, MITF⁺), T cells(CD3⁺) and cytotoxic T cells (CD8⁺) the rest were defined asuncharacterized.

To explore the association between the program markers and the “cold”phenotype, Applicants first generated a Delaunay neighborhood graph foreach image (linking cells that are immediate neighbors) and computed theobserved frequency of cell-to-cell interaction compared to that expectedby chance, as recently described (Goltsev et al., 2017). Malignant cellswere significantly more likely to reside next to other malignant cells,and significantly less likely to reside next to T cells (P<1*10⁻¹⁶,binomial test, Methods). Next, for each frame in the imaged section(1,377 cells/frame on average; Methods), Applicants computed thefraction of T cells and the average expression of the different markersin the malignant cells. Applicants then quantified the associationbetween expression of the immune resistance program markers and T cellinfiltration levels across frames from the different images (Methods).Confirming this analysis approach, malignant cells in highly infiltratedniches had significantly higher levels of HLA-A (FIG. 47A, P=2.61*10⁻⁴⁶,mixed-effects). Moreover, in line with the predictions, malignant cellsin cold/hot niches had significantly lower/higher levels of c-Jun(repressed in the resistance program), respectively (FIG. 47B,P=2.85*10⁻¹², mixed-effects), whereas p53, induced in the resistanceprogram) characterized cold niches (P=6.16*10⁻⁷, mixed-effects).Applicants do note, however, that LAMP2 expression (repressed in theresistance program) was also associated with cold niches, potentiallydue to its post-transcriptional regulation (Feng et al., 2015).

Finally, since only a few markers were analyzed in situ, Applicantstested whether scRNA-seq and multiplex in situ protein profiles can becombined to jointly learn cell states, using a variant of canonicalcorrelation analysis (CCA) (Butler and Satija, 2017) (Methods). Thecells were primarily embedded and clustered based on their cell types,and not according to source, confirming the congruence of the twodatasets, and that the markers tested can link global transcriptionalcell states to spatial organization in tissue (FIGS. 47C,D and 53).Taken together, these results support the association between theexpression of the immune resistance program and the cold phenotype.

The Immune Resistance Program is Intrinsic in Melanoma Cells Prior toTreatment and is Enhanced Specifically Post-Immunotherapy

Applicants hypothesized that the immune resistance program, while morepronounced in the malignant cell of patients after ICI, in fact reflectsan intrinsic resistance mechanism, present even before immunotherapy.First, the program is detected in TCGA tumors, which were all untreated.Second, while the program is more predominant in the malignant cells ofthe post-treatment resistant patients, it is also overexpressed in asubset of the malignant cells from untreated patients (FIGS. 44E and45C, right plots). This is aligned with clinical observations thatintrinsic ICI resistance is more prevalent than acquired ICI resistance(Sharma et al., 2017). However, because the scRNA-seq cohort did notinclude matched samples from the same patient or pre-treatment tumorsfrom subsequent responders vs. non-responders, Applicants could notdirectly distinguish intrinsic resistance from post-treatment effects.

To test this hypothesis, Applicants therefore analyzed an independentcohort of 90 specimens collected from 26 patients with metastaticmelanoma who underwent ICI therapy, with bulk RNA-Seq from biopsiescollected pre-treatment (n=29), on-treatment (n=35), and at the time ofprogression (n=26) (FIG. 44A, validation cohort 1). Applicants testedfor changes in the program score during the course of treatment, whileaccounting for tumor composition (Methods). The program was induced inon- and post-treatment samples compared to pre-treatment samples fromthe same patient (P=1.36*10⁻⁴ and 4.98*10⁻², immune resistance program,refined and non-refined, respectively, mixed-effect test, Methods),consistent with its overexpression in individual post-ICI malignantcells in the unmatched single-cell cohort (FIGS. 44E and 45C). However,inter-patient variation in the program's expression was significantlyhigher than these intra-patient changes (P<10⁻⁸, ANOVA). This suggestedthat the major differences between the post-treatment and untreatedtumors in the single-cell cohort reflect, at least in part, intrinsicdifferences between the two groups, which preceded the treatment, whichApplicants turned to assess in a second validation cohort (below).Notably, Applicants did not observe an induction in the programfollowing RAF/MEK-inhibition, indicating that the immune resistancestate it defines is specific to ICI therapy and not merely a genericmarker of any drug resistant tumor.

The Immune Resistance Program Predicts Patient Survival and ClinicalResponses to ICI

The association of the program with T cell infiltration, its functionalenrichment with immune evasion and exclusion mechanisms, its intrinsicexpression in some malignant cells prior to treatment, and its furtherinduction in post-ICI resistant lesions could make it a compellingbiomarker for response to immunotherapy. To test this hypothesis,Applicants examined the program in multiple independent cohorts.Applicants used both the full program and one refined to the subset ofgenes that are co-regulated (positively) or anti-regulated (negatively)with genes whose inhibition desensitized melanoma cells to T cellmediated killing in functional screens (Patel et al., 2017) (Table S6,Methods) (The exclusion and post-treatment programs show similar signalsand trends; FIGS. 48E-H and 54-55).

The underexpression of the program was strongly associated with improvedsurvival in 473 TCGA melanoma patients (who did not receive ICIimmunotherapy, FIGS. 48A and 54), even after controlling for tumorpurity and inferred T cell infiltration (Azimi et al., 2012; Bogunovicet al., 2009). Furthermore, combining the program with inferred T cellinfiltration levels yielded significantly more accurate predictions ofpatient survival than either alone (COX p-value=1.4*10⁻⁸, FIG. 48A,right). Other proposed mechanisms, such as de-differentiation ofmelanoma cells (Landsberg et al., 2012), as reflected by an MITF-lowsignature, and other malignant cell signatures (e.g., cell cycle or theAXL program) (Tirosh et al., 2016a), did not show an association withpatient survival, indicating that mere biological variation acrossmalignant cells is insufficient as a prognostic signature.

The program expression in published pre-treatment and early on-treatmentbulk expression profiles also distinguished eventual ICI responders fromnon-responders in those studies (FIGS. 48B,C). In a lung cancer mousemodel, the program expression in early on-treatment profiles clearlyseparated anti-CTLA-4 responders from non-responders (P=3.6*10⁻⁷,one-sided t-test, FIG. 48B) (Lesterhuis et al., 2015). In bulkpre-treatment RNA-Seq data from 27 melanoma patients that weresubsequently treated with Pembrolizumab (anti-PD-1) (Hugo et al., 2016),the program was underexpressed in the five complete responders, thoughjust above statistical significance (P=6.3*10⁻², one-sided t-test, FIG.48C). In bulk pre-treatment RNA-Seq data from 42 melanoma patients thatwere subsequently treated with the CTLA-4 inhibitor ipilimumab (VanAllen et al., 2015), the program was significantly lower in the twocomplete responders (P=5.2*10⁻³, one-sided t-test).

To test the predictive value of the program in a larger independentsetting, Applicants assembled a validation cohort of 112 patients withmetastatic melanoma who underwent a pre-treatment biopsy and bulkRNA-Seq followed by Pembrolizumab (anti-PD-1) therapy (FIG. 44A,validation cohort 2, Table S1). The cohort was collected in a differenthospital and country (Germany; Methods), and samples were processed andsequenced on the same platform (Methods). Applicants evaluated theprogram's performance in predicting anti-PD-1 responses as reflected by:(1) progression-free survival (PFS, recorded for 104 of the 112patients), (2) clinical benefit (CB, defined as either partial orcomplete response by RECIST criteria), and (3) complete response (CR)(Methods). Applicants also compared the performance of the predictors tothose of 32 other signatures, including the top hits of two functionalCRISPR screens of resistance to T cells and ICI (Manguso et al., 2017;Patel et al., 2017) (Table S10, Methods).

The programs were predictive of PFS in the validation cohort (FIGS. 48Dand 55A-E), even when accounting for other known predictors of ICIresponse, including inferred T cell infiltration levels and PD-L1expression (FIG. 55E). Although cell cycle alone is not associated withPFS (COX P >0.25), filtering the cell-cycle component from the programscore (Methods, and below) further improved PFS predictions (FIG. 48D,right), suggesting that a tumor's immune resistance should be evaluatedconditioning on its proliferation level. The program had a strongpredictive value beyond T cell infiltration (P=3.37*10⁻⁶,Wilcoxon-ranksum test), and was the only one negatively associated withPFS. Other alternative signatures were either not predictive or did notprovide any additive predictive value once accounting for T cellinfiltration levels (FIG. 48E).

The program was underexpressed in patients with clinical benefit (CB)compared to those without benefit (no-CB) (FIG. 48F). Nevertheless, somepatients with clinical benefit had high pre-treatment expression of theprogram. Applicants hypothesized that these patients might cease torespond quickly, due to pre-existing intrinsically resistant cells, likethose Applicants observed in the single-cell cohort and in validationcohort 1. Indeed, among patients with clinical benefit, those with highexpression of the program pre-treatment were significantly more likelyto experience subsequent progressive disease (FIG. 48F), and those withrapid progression (CB<6 months) had the highest scores of the program,even compared to those with no clinical benefit. Consistently, theprogram was most accurate in predicting patients with complete responses(P<6.31*10⁻³, one-sided t-test, FIGS. 48G and 55F), outperforming allthe other predictors (P=1.64*10⁻⁸, Wilcoxon ranksum test), all of which,including clinically-used markers and inferred T cell infiltrationlevels, failed to predict complete response (FIG. 4811).

The Immune Resistance Program is Coherently Controlled by CDK4/6

Applicants reasoned that the program could be a compelling drug target:it was identified by its association with a critical process—T cellexclusion—that affects resistance to immunotherapy; it is asignificantly predictive biomarker of ICI resistance; and it appears tobe coherently regulated, such that a shared control mechanism could betargeted to reverse it.

To this end, Applicants identified drugs that were significantly moretoxic to cell lines overexpressing the immune resistance program(controlling for cancer types, Methods), according to the efficacymeasures of 131 drugs across 639 human cancer cell lines (Garnett etal., 2012). The top scoring drug was the CDK4/6-inhibitor palbociclib(P=6.28*10⁻⁶, mixed-effects). Furthermore, the efficacy of CDK4/6inhibition and the expression of the resistance program were alsocorrelated in a study where the efficacies of CDK4/6 inhibitorspalbociclib and abemaciclib were measured across a collection of cancercell lines (P=7.15*10⁻⁶, mixed-effects) (Gong et al., 2017).

Applicants further hypothesized that CDK4 and 6 may act as the masterregulators of the immune resistance program. First, both CDK4 itself andmultiple CDK target genes, are members of the of the induced program(FIG. 45C, Table S6). Second, the program is more pronounced in cyclingcells (where CDK4/6 are active), both within the same patient group andamong cells of the same tumor (FIGS. 44E, 45C, and 56A,B, P<10⁻¹⁶, mixedeffects model). Importantly, the program is not merely a proxy of thecell's proliferation state: there was no significant difference betweenthe fraction of cycling cells in untreated vs. post-treatment tumors(P=0.696, t-test), the program was nearly identical when identified onlybased on non-cycling cells, and—unlike the expression of the resistanceprogram—the expression of cell cycle signatures was not associated withthe efficacy of CDK4/6 inhibitors across the cell lines. Finally,Applicants analyzed recently published expression profiles (Goel et al.,2017) of breast cancer cell lines and in vivo mouse models and foundthat CDK4/6 inhibition by abemaciclib represses the program (FIGS. 49A-Cand 56C). Thus, multiple lines of evidence suggest that CDK4/6inhibition could repress the expression of the immune resistance programand shift the cancer cell population to a less immune resistant state.

CDK4/6 Inhibitors Repress the Immune Resistance Program in MalignantMelanoma Cells

To test this hypothesis, Applicants studied the effect of abemaciclib onthe immune resistance program in melanoma cell lines. Applicantsselected three melanoma cell lines from the Cancer Cell LineEncyclopedia (Barretina et al., 2012) with a strong expression of theresistance program (Table S12), two of which are RB1-sufficient (IGR37,UACC257) and one is RB1-deficient (A2058). Applicants profiled each cellline with scRNA-seq before and after treatment with abemaciclib for 1week (FIGS. 49D-E), analyzing over 23,000 cells in these and follow-upconditions (below).

TABLE S12 The overall expression (OE) of the immune resistance signatureacross the CCLE melanoma cell lines. Melanoma cell Immune lineresistance OE HMCB 0.818 LOXIMVI 0.72 UACC257 0.706 CHL1 0.698 IGR370.57 MELHO 0.522 COLO741 0.5 G361 0.476 COLO679 0.468 A2058 0.465 SKMEL30.443 GRM 0.431 SKMEL30 0.405 MEWO 0.371 A375 0.368 HS936T 0.339 K029AX0.308 IPC298 0.261 IGR1 0.243 SKMEL1 0.238 SKMEL5 0.182 COLO783 0.174COLO849 0.082 CJM 0.06 MELJUSO 0.049 COLO792 0.041 UACC62 0.015MDAMB435S 0.005 IGR39 0 WM2664 −0.015 WM88 −0.045 HS944T −0.053 RPMI7951−0.067 WM983B −0.09 WM1799 −0.091 A101D −0.097 HS895T −0.126 SKMEL28−0.152 SH4 −0.226 RVH421 −0.227 HT144 −0.23 SKMEL2 −0.242 COLO800 −0.251HS294T −0.264 WM793 −0.265 HS852T −0.341 HS934T −0.368 COLO829 −0.377HS839T −0.386 C32 −0.427 HS940T −0.434 HS688AT −0.435 HS939T −0.464HS600T −0.464 COLO818 −0.466 HS695T −0.5 WM115 −0.513 MALME3M −0.607SKMEL31 −0.759 SKMEL24 −0.975

Consistent with the hypothesis, only in the RB-sufficient cell lines,abemaciclib dramatically decreased the proportion of cellsoverexpressing the immune resistance program and induced an immuneresponse in the surviving cells. In the RB1-sufficient lines, IGR37 andUACC257, 10% of the cells had exceptionally strong expression of theimmune resistance program (“immune resistant” cells) prior to treatment,decreasing to 2% and 1% of cells post-treatment, respectively(P<1*10⁻³⁰, hypergeometric test) (FIGS. 49D,E). In contrast, in theRB1-deficient line A2058 the treatment did not repress the immuneresistant state (P >0.5, one-sided t-test), consistent with thehypothesis that CDK4/6 inhibitors depend on RB1-sufficiency.

Moreover, in the two RB-sufficient lines, the remaining cells thatunderexpressed the immune resistance program, underwent substantialtranscriptional changes, including the induction of key repressedcomponent of the immune resistance program, such as the SASP. Inparticular, abemaciclib repressed the expression of DNMT1(P<2.23*10⁻¹⁰⁶, likelihood-ratio test), consistent with previousobservations (Goel et al., 2017) that CDK4/6 inhibition leads to DNMT1repression, allowing the methylation of endogenous retroviral genes(ERVs), which in turn triggers a double-stranded RNA (dsRNA) responseand stimulates type III IFN production (Goel et al., 2017). Followingabemaciclib treatment there was also a higher portion of cells withincreased expression of a MITF program (Tirosh et al., 2016a), which isrepressed in “immune resistant” cells (P<3.33*10⁻¹⁵, hypergeometrictest, FIG. 49D,E).

In particular, abemaciclib induced SASP, which is a major repressedcomponent in the resistance program. First, the SASP module wassignificantly induced at the transcriptional level (P<3.91*10⁻¹²,hypergeometric test, FIGS. 49D,E). Moreover, when Applicants measured 40human cytokines and chemokines in the conditioned media of abemaciclibtreated cancer cells, Applicants found it induced several secretedfactors (FIG. 49F), including macrophage inhibition factor (MIF), CX3CL1(which induces migration and adhesion of T and NK cells and is linked toclinical outcomes in immunotherapy treatment (Herbst et al., 2014;Nelson and Muenchmeier, 2013)), and CCL20 (an important factor for Tcell differentiation, which may enhance immunity in melanoma (Gordy etal., 2016)). Consistently, abemaciclib also induced alpha-galactosidaseactivity and morphological alterations that reflect cellular senescence(FIG. 49G). Thus, unlike the mechanism described in breast cancer cells(Goel et al., 2017), abemaciclib might trigger SASP and celldifferentiation in malignant melanoma cells.

Finally, Applicants tested if the effect of abemaciclib treatment onmalignant cells is impacted by the presence of tumor infiltratinglymphocytes (TILs) in a patient-derived co-culture model of melanomacells and ex vivo expanded TILs from the same metastatic melanomalesion. After treating the malignant cells with abemaciclib for oneweek, Applicants added autologous TILs to the cultures. Applicantscompared scRNA-seq profiles between these melanoma cells and cells fromsimilar co-cultures but without abemaciclib treatment, or from cultureswith neither abemaciclib treatment nor TILs. Exposure to TILs reducedthe expression of the immune resistance program, both in the control andin the abemaciclib-treated cells (P<9.85*10⁻¹⁴, one-sided t-test).Abemaciclib further intensified these effects, as it further repressedthe immune resistance program in both conditions (with and without theexposure to TILs, P<3.60*10⁻⁷, one-sided t-test).

Discussion

Most melanoma patients have either intrinsic or acquired resistance toICI, yet the systematic characterization of molecular resistancemechanisms has been limited. Here, Applicants leverage clinicalscRNA-seq data and multiple cohorts to map malignant cell statesassociated with resistance to ICI, revealing a coherently co-regulatedprogram that may be therapeutically targeted to overcome immune evasionand suppression.

The malignant cell resistance program showed prognostic and predictivepower in several independent ICI cohorts, including a large newclinically annotated cohort of patients with pre-treatment (anti-PD-1)biopsies profiled by RNA-seq. The program outperformed other publishedbiomarkers in the space, and may help to prospectively stratify patientsto clinical trials and therapies. Even though the program was initiallyderived, in part, based on associations with inferred T cellinfiltration levels, unlike many other biomarkers, it has a significantpredictive value beyond T cell infiltration.

The program Applicants uncovered is primarily associated with intrinsicICI resistance. It is manifested also in malignant cells of untreatedpatients in the single-cell cohort, and in bulk RNA-seq data from threeindependent cohorts of untreated patients: TCGA, a longitudinal cohortof ICI-treated patients (validation cohort 1), and a cohort of 112pre-ICI patients (validation cohort 2). Among single cells ofpre-treated patients, a subset (20.9% cells from 10 different patients)already overexpresses the program. In bulk samples collected before andafter ICI, inter-patient variation exceeded intra-patient variation,further supporting an intrinsic role. In 112 melanoma patients, thispre-ICI inter-patient variation is tightly associated with ICIresponses. Finally, the program is more pronounced after ICI failure,but not post targeted therapy, and thus it is unlikely to merely reflectthe impact of any therapeutic intervention.

Some of the concepts established for drug resistance to targeted cancertherapies with RAF/MEK-inhibitors in melanoma may also be applicable toimmunotherapies. Similar to the presence of a small sub-population ofcells expressing a MITF-low program, which confers resistance toRAF/MEK-inhibitors, and rises in frequency under the pressure of a drug(Shaffer et al., 2017; Tirosh et al., 2016a, Hangauer et al., 2017;Viswanathan et al., 2017), patient tumors who have not been treated withICI contain some cells expressing the immune resistance program. It isplausible that these cells are responsible for either intrinsicresistance to ICI or lie in protected niches, and thus emerge in thecontext of ICI resistance. Selective targeting of these cells incombination with ICI may delay or prevent ICI resistance.

Applicants have focused on malignant cells, but T cell states or clones,beyond their extent of infiltration, might also predict the success ofICI. Within the limitation of the unmatched single-cell cohort,comparing the individual T cells of untreated vs. post-treatment(resistant) patients, suggested that treatment has activated the T cellsand caused their expansion (data not shown). While Applicants cannotrule out the presence of other intrinsic T cell dysfunction mechanisms,this is consistent with a model where, at least partly, malignant cellscause ICI resistance despite at least some T cell functionality.

Because of the potential functional role of the program and its coherentunderlying regulation, compounds that repress it may sensitize malignantcells to immunotherapy and/or T-cell mediated killing (FIG. 50),especially in patients with a high intrinsic (pre-ICI) expression of theimmune resistance program. Based on a systematic analysis of drugefficacies and the program features Applicants hypothesized that CDK4/6inhibition could have such a sensitizing effect, and tested this inmalignant melanoma cell lines and in co-cultures of patient cells withautologous TILs. CDK4/6 inhibition reversed the resistanttranscriptional state: subpopulations of highly immune resistant cancercells were dramatically reduced, either because the drug selectivelyeradicated them or because it triggered them to adopt a less immuneresistant state. In parallel, CDK4/6 inhibition triggered the melanomacells to adopt a senescent-like phenotype accompanied by secretion ofkey chemokines, which have been previously shown to enhance T cellresponses (Gordy et al., 2016; Herbst et al., 2014; Nelson andMuenchmeier, 2013).

The malignant resistance programs may be relevant in other subtypes ofmelanoma as well as in other tumor types. Among different types ofmelanoma, uveal melanoma has more active resistance programs compared tocutaneous melanoma (FIG. 46A); across cancers, the immune resistanceprogram is lower in some of the more responsive tumors (head and neck,kidney, skin, lung) and higher in tumor types that are less responsiveto immunotherapy and/or arise from immune-privileged tissues (eye,testis) (FIG. 57). Interestingly, synovial sarcoma, which is driven by asingle genomic aberration in the BAF complex, has the highest resistancescores. The BAF complex has been recently shown to play a key role inresistance to ICI immunotherapy (Miao et al., 2018; Pan et al., 2018).While this pan-cancer analysis is intriguing, it may still be impactedby the composition of the tumor microenvironment, which is challengingto control without single-cell data.

Future similar studies of other tumors could apply the approach toidentify other tumor-specific resistance programs. For example,Applicants performed such analysis with the recent head and neck cancersingle cell cohort (Puram et al., 2017) and found that CAFs in coldtumors overexpressed genes up-regulated by TGFB1 (P=1.70*10⁻⁷,hypergeometric test). Indeed, TGFB1 and TGFB signaling has been recentlyshown to be highly associated with lack of response to anti-PD-L1treatment in urothelial cancer patients (Mariathasan et al., 2018). Inline with the findings, co-administration of TGFβ-blocking andanti-PD-L1 has been shown to modulated the tumor CAFs, which in turnfacilitated T cell infiltration and tumor regression in mouse models(Mariathasan et al., 2018).

Overall, the analysis sheds light on the way cells shape and are beingshaped by their microenvironment in tumors, and the approaches can beapplied in other tumors to systematically map immune resistant malignantcell states, uncover improved biomarkers for patient selection, andreveal principles for the development of new therapeutics.

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Example 10—Materials and Methods Patients

For the discovery cohort (single cell RNA-Seq), tissue was procuredunder Institutional Review Board (IRB) approved protocols at Brighmanand Women's Hospital and Dana-Farber Cancer Institute, Boston, Mass.Patients were consented to these protocols (11-104) in clinic visitsprior to surgery/biopsy. Patients included in the initial study andnewly collected specimens are highlighted in table S1.

For validation cohorts (bulk-RNA-seq), patient tissue was collectedunder IRB protocols of the University Hospital Essen, Germany andMassachusetts General Hospital, Boston, Mass. (protocol 11-181) and TheWistar Institute, Philadelphia, Pa. (Human subjects protocol 2802240).Demographics of validation Cohort 1 are summarized in table S1.Validation Cohort 2 included 90 samples from 26 patients, with multiplebiopsies per patient, taken before, during, or after various treatmentregimens, including both targeted therapies and immunotherapies. Twelvepatients had both pre- and post/on-ICI samples (table S1).

Tissue Handling and Tumor Disaggregation

Resected tumors were transported in DMEM (ThermoFisher Scientific,Waltham, Mass.) on ice immediately after surgical procurement. Tumorswere rinsed with PBS (Life Technologies, Carlsbad, Calif.). A smallfragment was stored in RNA-protect (Qiagen, Hilden, Germany) for bulkRNA and DNA isolation. Using scalpels, the remainder of the tumor wasminced into tiny cubes <1 mm³ and transferred into a 50 ml conical tube(BD Falcon, Franklin Lakes, N.J.) containing 10 ml pre-warmed M199-media(ThermoFisher Scientific), 2 mg/ml collagenase P (Roche, Basel,Switzerland) and 10 U/μl DNase I (Roche). Tumor pieces were digested inthis media for 10 minutes at 37° C., then vortexed for 10 seconds andpipetted up and down for 1 minute using pipettes of descending sizes (25ml, 10 ml and 5 ml). If needed, this was repeated twice more until asingle-cell suspension was obtained. This suspension was then filteredusing a 70 μm nylon mesh (ThermoFisher Scientific) and residual cellclumps were discarded. The suspension was supplemented with 30 ml PBS(Life Technologies) with 2% fetal calf serum (FCS) (Gemini Bioproducts,West Sacramento, Calif.) and immediately placed on ice. Aftercentrifuging at 580 g at 4° C. for 6 minutes, the supernatant wasdiscarded and the cell pellet was re-suspended in PBS with 1% FCS andplaced on ice prior to staining for FACS.

FACS

Single-cell suspensions were stained with CD45-FITC (VWR, Radnor, Pa.)and live/dead stain using Zombie Aqua (BioLegend, San Diego, Calif.) permanufacturer recommendations. First, doublets were excluded based onforward and sideward scatter, then Applicants gated on viable cells(Aqua^(low)) and sorted single cells (CD45⁺ or CD45⁻) into 96-wellplates chilled to 4° C., pre-prepared with 10 μl TCL buffer (Qiagen)supplemented with 1% beta-mercaptoethanol (lysis buffer). Single-celllysates were sealed, vortexed, spun down at 3,700 rpm at 4° C. for 2minutes, placed on dry ice and transferred for storage at −80° C.

Single Cell RNA-seq

Whole Transcriptome amplification (WTA) was performed with a modifiedSMART-Seq2 protocol, as described previously (1, 2) with Maxima ReverseTranscriptase (Life Technologies) instead of Superscript II. Next, WTAproducts were cleaned with Agencourt XP DNA beads and 70% ethanol(Beckman Coulter, Brea, Calif.) and Illumina sequencing libraries wereprepared using Nextera XT (IIlumina, San Diego, Calif.), as previouslydescribed (2). The 96 samples of a multiwell plate were pooled, andcleaned with two 0.8×DNA SPRIs (Beckman Coulter). Library quality wasassessed with a high sensitivity DNA chip (Agilent) and quantified witha high sensitivity dsDNA Quant Kit (Life Technologies).

For droplet-based scRNA-seq, experiments were performed on the 10×Genomics Chromium platform, with the Chromium Single Cell 3′ Library &Gel Bead Kit v2 and Chromium Single Cell 3′ Chip kit v2 according to themanufacturer's instructions in the Chromium Single Cell 3′ Reagents KitsV2 User Guide. Briefly, 6,000 cells were re-suspended in PBSsupplemented with 0.04% BSA and loaded to each channel. The cells werethen partitioned into Gel Beads in Emulsion in the GemCode instrument,where cell lysis and barcoded reverse transcription of RNA occurred,followed by amplification, shearing and 5′ adaptor and sample indexattachment.

Barcoded single cell transcriptome libraries were sequenced with 38 bppaired end reads on an Illumina NextSeq 500 Instrument.

RNA-Capture and Bulk RNA-Seq of Validation Cohorts

RNA extraction from formalin-fixed, paraffin-embedded (FFPE) tissueslides was performed by the Genomics Platform of the Broad Institute(Cambridge, Mass.). For cDNA Library Construction total RNA was assessedfor quality using the Caliper LabChip GX2 (Perkin Elmer). The percentageof fragments with a size greater than 200 nt (DV200) was calculated andan aliquot of 200 ng of RNA was used as the input for first strand cDNAsynthesis using Illumina's TruSeq RNA Access Library Prep Kit. Synthesisof the second strand of cDNA was followed by indexed adapter ligation.Subsequent PCR amplification enriched for adapted fragments. Theamplified libraries were quantified using an automated PicoGreen assay(Thermo Fisher Scientific, Cambridge, Mass.). 200 ng of each cDNAlibrary, not including controls, were combined into 4-plex pools.Capture probes that target the exome were added, and hybridized for therecommended time. Following hybridization, streptavidin magnetic beadswere used to capture the library-bound probes from the previous step.Two wash steps effectively remove any nonspecifically bound products.These same hybridization, capture and wash steps are repeated to assurehigh specificity. A second round of amplification enriches the capturedlibraries. After enrichment, the libraries were quantified with qPCRusing the KAPA Library Quantification Kit for Illumina SequencingPlatforms (Illumina) and then pooled equimolarly. The entire process isperformed in 96-well format and all pipetting is done by either AgilentBravo or Hamilton Starlet. Pooled libraries were normalized to 2 nM anddenatured using 0.1 N NaOH prior to sequencing. Flowcell clusteramplification and sequencing were performed according to themanufacturer's protocols using Illumina HiSeq 2000 or 2500 (Illumina).Each run was a 76 bp paired-end with an eight-base index barcode read.Data was analyzed using the Broad Picard Pipeline(broadinstitute.github.io/picard/) which includes de-multiplexing anddata aggregation.

RNA-Seq Data Pre-Processing

BAM files were converted to merged, demultiplexed FASTQ files. Thepaired-end reads obtained with the SMART-Seq2 protocol were mapped tothe UCSC hg19 human transcriptome using Bowtie (Langmead et al., 2009),and transcript-per-million (TPM) values were calculated with RSEM v1.2.8in paired-end mode (Li and Dewey, 2011). The paired-end reads obtainedwith the 10× Genomics platform were mapped to the UCSC hg19 humantranscriptome using STAR (Dobin et al., 2013), and gene counts/TPMvalues were obtained using the 10× Genomics computational pipeline(cellranger-2.1.0).

For bulk RNA-Seq data, expression levels of genes were quantified asE_(i,j)=log₂(TPM_(i,j)+1), where TPM_(i,j) denotes the TPM value of genei in sample j. For scRNA-seq data, expression levels were quantified asE_(i,j)=log₂(TPM_(i,j)/10+1), where TPM_(i,j) denotes the TPM value ofgene i in cell j. TPM values were divided by 10 because the complexityof the single-cell libraries is estimated to be within the order of100,000 transcripts. The 10⁻¹ factoring prevents counting eachtranscript ˜10 times, which would have resulted in overestimating thedifferences between positive and zero TPM values. The average expressionof a gene i across a population of N cells, denoted here as P, wasdefined as

$E_{i,p} = {\log^{2}\left( {1 + \frac{\sum_{j \in P}{TPM}_{i,j}}{N}} \right)}$

For each cell, Applicants quantified the number of genes with at leastone mapped read, and the average expression level of a curated list ofhousekeeping genes (Tirosh et al., 2016a). Applicants excluded all cellswith either fewer than 1,700 detected genes or an average housekeepingexpression (E, as defined above) below 3 (Table S2). For the remainingcells, Applicants calculated the average expression of each gene(E_(p)), and excluded genes with an average expression below 4, whichdefined a different set of genes in different analyses depending on thesubset of cells included. In cases where Applicants analyzed differentcell types together, Applicants removed genes only if they had anaverage E_(p) below 4 in each of the different cell types that wereincluded in the analysis. When analyzing CD45⁺ cells, Applicantsexcluded genes as described above only after the assignment of cells tocell types in order to prevent the filtering of genes that wereexpressed by less abundant cell types.

Data Imputation and Normalization

In all differential expression analyses, Applicants first modeled theread counts as a mixture of a negative binomial (NB) and Poissoncomponents to estimate the expression levels, using SCDE (6) with thecode provided in github.com/hms-dbmi/scde. The resulting normalized andimputed expression matrix, denoted as E′, was used in the differentialexpression analyses because, for single genes, it provides a moreaccurate and sensitive estimation of their expression. Analysis ofdroplet-based scRNA-seq data (10× Genomics Chromium, above) wasperformed with the Seurat package (http://www.satijalab.org/seurat),using the likelihood-ratio test for differential gene expressionanalyses (McDavid et al., 2013).

Identifying Cell States Associated with Specific Tumor Compositions

Applicants combined scRNA-seq and bulk RNA-Seq data to characterize thestate of a specific cell type in tumors with a specific cell typecomposition (FIG. 1B). The method takes as input scRNA-seq data and acohort of RNA-Seq data, both collected from tumors of the same cancertype. For clarity Applicants describe the approach for malignant cellsand T cells as applied here, though it can be applied to any pair ofcell types.

1. Analyze the scRNA-seq data: (a) assign cells to cell types (seesections: Classification of malignant and stromal cells andClassification of immune cells); and (b) define a signature of malignantcells and a signature of T cells, consisting of genes which areprimarily (specifically) expressed by malignant cells or T cells,respectively (see section: Cell-type specific signatures).

2. Analyze the bulk RNA-Seq data: (a) estimate the T cell infiltrationlevel in each tumor by computing the overall expression (OE, seesection: Computing the OE of gene signatures) of the T cell signature ineach bulk sample; (b) compute the Spearman correlation coefficientbetween the expression of each of the genes in the malignant signatureand the OE of the T cell signature across the bulk tumors; and (c)define the seed exclusion-up (down) signature as the top 20 malignantgenes that are significantly negatively (positively) correlated in (b).

3. Analyze the scRNA-seq data of the malignant cells: (a) compute the OEof the seed exclusion signatures in each of the malignant cells; (b)compute the partial Spearman correlation coefficient between theexpression of each gene and the OE of the seed exclusion signaturesacross the single malignant cells, while controlling for technicalquality (the number of reads and genes that were detected in the cells).

4. Derive the final genome-scale exclusion signatures, defined as: (i)exclusion-up: genes which were significantly positively correlated withthe seed exclusion-up signature and significantly negatively correlatedwith the seed exclusion-down signature in the analysis described in (3);and (ii) exclusion-down: genes which were significantly positivelycorrelated with the seed exclusion-down signature and significantlynegatively correlated with the seed exclusion-up signature in theanalysis described in (3). In this analysis, a gene is defined assignificantly correlated with a signature if it was among the 200topmost correlated genes, with p-value <10⁻¹⁰, and Pearson |r|>0.1.Applicants implemented the approach with our clinical scRNA-seq melanomadata and bulk RNA-Seq data of 473 Skin Cutaneous Melanoma (SKCM) tumorsfrom TCGA (as provided in xenabrowser.net/datapages/).

Computing the OE of Gene Signatures

Gene modules are more robust to noise and provide more coherent signalsthan the expression of single genes (Shalek et al., 2013, 2014; Wagneret al., 2016). To compute the OE of a gene module or signatureApplicants used a scheme that filters technical variation and highlightsbiologically meaningful patterns. The procedure is based on the notionthat the measured expression of a specific gene is correlated with itstrue expression (signal), but also contains a technical (noise)component. The latter may be due to various stochastic processes in thecapture and amplification of the gene's transcripts, sample quality, aswell as variation in sequencing depth (Wagner et al., 2016). Thesignal-to-noise ratio varies, depending, among other variables, on genetranscript abundance.

Applicants therefore computed the OE of gene signatures in a way thataccounts for the variation in the signal-to-noise ratio across genes andcells. Given a gene signature and a gene expression matrix E (as definedabove), Applicants first binned the genes into 50 expression binsaccording to their average expression across the cells or samples. Theaverage expression of a gene across a set of cells within a sample isE_(i,p) (see: RNA-Seq data pre-processing) and the average expression ofa gene across a set of N tumor samples was defined as:

${_{j}\left\lbrack E_{ij} \right\rbrack} = {\sum_{j}{\frac{E_{ij}}{N}.}}$

Given a gene signature S that consists of K genes, with kb genes in binb, Applicants sample random S-compatible signatures for normalization. Arandom signature is S-compatible with signature S if it consists ofoverall K genes, such that in each bin (b) it has exactly kb genes. TheOE of signature S in cell or sample j is then defined as:

${OE}_{j} = \frac{\sum_{i \in S}C_{ij}}{_{\overset{\sim}{S}}\left\lbrack {\sum_{i \in \overset{\sim}{S}}C_{ij}} \right\rbrack}$

Where {tilde over (S)} is a random S-compatible signature, and C_(ij) isthe centered expression of gene i in cell or sample j, defined asC_(ij)=E_(ij)−

[E_(ij)]. Because the computation is based on the centered geneexpression matrix C, genes that generally have a higher expressioncompared to other genes will not skew or dominate the signal.

Applicants found that 100 random S-compatible signatures are sufficientto yield a robust estimate of the expected value

_({tilde over (S)})[Σ_(i∈{tilde over (S)})C_(ij)]. The distribution ofthe OE values was normal or a mixture of normal distributions, and,unlike the expression of a single gene, fulfilled the assumptions of themixed effects models or hierarchal linear models that Applicants appliedto study the differential expression of gene signatures (as described inthe Inter-patient single-cell differential expression analysis of genesets section).

In cases where the OE of a given signature has a bimodal distributionacross the cell population, it can be used to naturally separate thecells into two subsets. To this end, Applicants applied the ExpectationMaximization (EM) algorithm for mixtures of normal distributions todefine the two underlying normal distributions. Applicants then assignedcells to the two subsets, depending on the distribution (high or low)that they were assigned to.

Applicants use the term a transcriptional program (e.g., the immuneresistant program) to characterize cell states which are defined by apair of signatures, such that one (S-up) is overexpressed and the other(S-down) is underexpressed. Applicants define the OE of such cell statesas the OE of S-up minus the OE of S-down.

Classification of Malignant and Stromal Cells

In the non-immune compartment (CD45⁻ cells), Applicants distinguishedmalignant and non-malignant cells according to three criteria: (1) theirinferred CNV profiles (5, 10); (2) under-expression of differentnon-malignant cell-type signatures; and (3) higher similarity tomelanoma tumors than to adjacent normal tissue, based on the comparisonto bulk RNA-seq profiles. Specifically: (1) to infer CNVs from thescRNA-Seq data Applicants used the approach described in (10) asimplemented in the R code provided in github.com/broadinstitute/inferCNVwith the default parameters. Cells with an average absolute CNV levelthat was below the 0.1 quantile of the entire CD45⁻ cell population wereconsidered as potentially non-malignant according to this criterion. (2)Applicants used signatures of endothelial cells, stromal cells, andCancer Associated Fibroblasts (CAFs), as provided in table S3. Thesignatures combine well-established markers from two sources(www.biolegend.com/cell_markers and (5)). Applicants computed the OE ofthese three signatures in each of the CD45⁻ cells, while controlling forthe impact of technical cell quality (as described in section OverallExpression (OE) of gene signatures). CD45⁻ cells that expressed any oneof these three signatures above the 0.95 quantile were considered asnon-malignant according to this criterion. (3) Applicants downloaded thepan-cancer TCGA RNA-seqV2 expression data from xena.ucsc.edu, and log2-transformed the RSEM-based gene quantifications. For each cell,Applicants computed the Spearman correlation between its profile (inTMP) and each bulk profile (in TPM) of 473 skin cutaneous melanomasamples and 727 normal solid tissues. Applicants then tested, for eachcell, if it was more similar to the melanoma tumors compared to thenormal tissues, by applying a one-sided Wilcoxon ranksum test on thecorrelation coefficients that were obtained for that cell. Cells thatwere more similar to the normal tissues (P<0.05, Wilcoxon ranksum test)were considered as potentially non-malignant according to thiscriterion.

The cell assignments that were obtained by these three differentcriteria were highly consistent (Figures S1A,S1B, hypergeometric p-value<10⁻¹⁷). Cells that were identified as potentially nonmalignantaccording to one or more of these three criteria were defined asnonmalignant, and were omitted from further analyses of the malignantcells. The nonmalignant CD45⁻ cells were further classified into CAFsand endothelial cells, if they overexpressed only one of thecorresponding gene signatures, and as unresolved cells otherwise.

TABLE S3 Table S3. Cell type signatures that were used for cellclassification. Endothelial Stromal Mast Cell Cell CAF Basophile B cellEosinophil Erythrocyte Cell MDSC VWF MMP2 FAP ANPEP BLK C3AR1 CD24 ENPP3CCR7 TEK ICAM3 THY1 CCR3 CD19 C5AR1 GYPA KIT CD1A MCAM TLR3 DCN CD44 CD2CCR1 PTPRC CD1B CD34 MADCAM1 COL1A1 CD63 CD22 CCR3 CD1C CD68 CD80 CD86CSF1R ENG FCGR1A FUT4 ITGAL ITGAM CD3E CD3G CD8A CD8B CST7 GZMA GZMB1FNG NKG7 ENTPD1 FOXP3 IKZF2 IL2RA ISG20 ITGAE LAG3 LRRC32 NT5E CD3DCD3E CD3G CD4 CD3E CD3G CD4 IL17A IL17F IL1R1 IL21 IL22 KLRB1 GATA3 1RF4STAT6 CD4 CSF2 CXCR4 GATA3 HAVCR1 ICOS IL10 IL13 IL1R1 CXCR3 DPP4 HAVCR2IFNA1 IFNGR1 IL2 KLRD1 TNF TNFSF11 CD4 CD40LG CD84 CXCR5 ICOS IL6R PDCD1SLAMF1 STAT3 CD151 CD226 CD36 CD46 CD47 CD48 CD63 CD69 CD84 CD4 CD40CD80 CD83 CD86 CD8A CLEC4C CMKLR1 IL3RA ITGA2 ITGAM ITGAX KLRA1 KLRB1KLRD1 KLRK1 NCAM1 NCR1 CEACAM8 CSF3R CXCR1 CXCR2 FCGR1A FUT4 ITGAM 1TGAXMME CD4 SELL CD207 CD209 CD4 CD40 CD80 CD83 CD86 CMKLR1 DCX ITGB3 PECAM1SELP CD207 CD209 CD4 CD40 CD80 CD83 CD86 CMKLR1 HLA- DOA CD244 CD52 CD53CXCR3 FCER2 FUT4 IL9R ITGA4 LAIR1 CD40 CD5 CD69 CD70 CD79A CD79B CD80CD86 CD93 CD69 ENPP3 ICAM1 IL3RA LAMP1 TLR4 COL1A2 COL6A1 COL6A2 COL6A3MMP1 PDGFRA TLR4 THY1 KIT TIMP1 ITGA4 MMP9 PDGFRB 1TGB3 PROCR CDH5 KDRSELE PECAM1 ENG ICAM 1 FLT4 NRP1 PDCD1LG2 Myeloid Plasmacytoid DendriticNaive Dendritic T Follicular Megakarocyte Cell T Cell Neutrophil NK CellCell Platelet Helper CD9 CCR7 CCR7 ANPEP B3GAT1 CCR7 BSG BCL6 GP1BA CD1ACD3D C5AR1 CD244 CD1A CCL5 CD3D ITGA2B CD1B CD3E CD14 CD69 CD1B CCR3CD3E ITGAV CD1C CD3G CD33 IL2RB CD1C CD109 CD3G ITGAX LAMP2 LILRB4 TLR2TLR4 PRF1 SELL TNFRSF18 TNFRSF4 LINC-ROR STAT3 IL4 IL5 IL6 PTGDR2 TNFSF4CD9 CNGB1 CSF3R FCGR2A FCGR2B GP1BA ICAM2 ITGA2 ITGA4 ITGAM ITGAX NRP1PDCD1LG2 TLR9 NKG2 SIGLEC7 SLAMF6 SLAMF7 PECAM 1 SELL TLR2 ITGA4 ITGAMITGAX LY75 NRP1 PDCD1LG2 HLA- HLA- HLA- HLA- HLA- ITGA4 ITGAM ITGAX DOBDRA DRB1 DRB5 DRB6 PTGDR2 S100A9 SIGLEC10 SIGLEC8 FCER2 MS4A1 PAX5 PDCD1SDC1 TNFRSF13B TNFRSF13C TNFRSF9 MME PECAM1 TIMP2 TLR1 1TGB1 ICAM1 ICAM2TLR2 VCAM1 ITGA6 ITGAV ITGB1 ITGB3 IAM3 LAMP2 LRRC32 LYN PECAM1 SELP SPNTNFSF14 VEGFA Th1 Th2 Th9 Th17 Th22 Treg Cytotoxic_T_cell MacrophageCCR1 CCR3 CD3D CCR4 AHR CCR4 CCL3 CCR5 CCR5 CCR4 CD3E CCR6 CCR10 CD4CCL4 CD14 CD4 CCR7 CD3G CD38 CCR4 CNGB1 CD2 CD163 CSF2 CCR8 CD4 CD3DCCR6 CTLA4 CD3D CD33 ITGA2B LY75 TNFSF4 VCAM1

The cell assignments that were obtained by these three differentcriteria were highly consistent (FIG. 5B, hypergeometric p-value<10⁻¹⁶). Cells that were identified as potentially non-malignantaccording to one out of these three criteria were defined asnon-malignant, and were omitted from further analyses of the malignantcells. The non-malignant cells were further classified into CAFs andendothelial cells, if they overexpressed the pertaining gene signatures,and as unresolved cells otherwise.

Classification of Immune Cells

To classify immune cells, Applicants first filtered CD45⁺ cells thatwere potentially malignant or doublets of immune and malignant cellsbased on their inferred CNV profiles. To this end, Applicants definedthe overall CATTY level of a given cell as the sum of the absolute CNVestimates across all genomic windows. For each tumor, Applicantsgenerated its CNV profile by averaging the CNV profiles of its malignantcells, when considering only those with the highest overall CNV signal(top 10%). Applicants then evaluate each cell by two values: (1) itsoverall CNV level, and (2) the Spearman correlation coefficient obtainedwhen comparing the cell CNV profile to the CNV profile of its tumor.These two values were used to classify cells as malignant,non-malignant, and unresolved cells that were excluded from furtheranalysis (FIG. 5C-E).

Next, Applicants applied two different clustering approaches to assignimmune (CD45⁺) cells into cell types. In the first approach, Applicantsclustered the CD45⁺ cells according to 194 well-established markers of22 immune cell subtypes (table S3; assembled fromwww.biolegend.com/cell_markers and (5)). The clustering was performed inthree steps: (1) Applicants computed the Principle Components (PCs) ofthe scRNA-seq profiles, while restricting the analysis to the 194biomarker genes. Applicants used the top PCs that captured more than 50%of the cell-cell variation. In this case 10 PCs were used, but theresults were robust and stable when using the first 5-15 PCs. (2)Applicants applied t-SNE (t-Distributed Stochastic Neighbor Embedding)to transform these first 10 PCs to a two-dimensional embedding, usingthe R implementation of the t-SNE method with the default parameters, asprovided in lvdmaaten.github.io/tsne/. (3) Applicants applied a densityclustering method, DBscan (11), on the two-dimensional t-SNE embeddingthat was obtained in (2). This process resulted in six clusters forwhich the top preferentially expressed genes included multiple knownmarkers of particular cell types (FIG. 6).

To map between clusters and cell types Applicants compared (one sidedt-test) each cluster to the other clusters according to the OE of thedifferent cell-type signatures (table S3). The cell-type signature thatwas most significantly (t-test p-value <10⁻¹⁰) overexpressed in thecluster compared to all other clusters was used to define the clusteridentity. In this manner, Applicants annotated the clusters as CD8 andCD4 T cells, B cells, macrophages, and neutrophils (FIG. 1C). Cells thatclustered with the CD8 T-cells and did not express CD8A or CD8B werelabeled as NK cells if they overexpressed NK markers, otherwise theywere considered as unresolved T-cells. T-cells that were clusteredtogether with the CD4 T-cells and expressed CD8A or CD8B were alsoconsidered as unresolved T-cells. Unresolved T-cells were not used infurther analyses of CD4 or CD8 T cells.

To assess the robustness of the assignments, Applicants applied anotherapproach, and determined the concordance between the two assignments. Inthe second approach, Applicants first made initial cell assignmentsbased on the OE of well-established cell-type makers: T-cells (CD2,CD3D, CD3E, CD3G), B-cells (CD19, CD79A, CD79B, BLK), and macrophages(CD163, CD14, CSF1R). Across all the CD45⁺ cells, the OE levels of thesesignatures had binomial distributions. Applicants used the bimodal OE ofeach signature to assign cells to cell types (as described in sectionOverall Expression (OE) of gene signatures). Cells that were assigned tomore than one cell type at this point were considered as unresolved.Cells that were defined as T-cells according to this measure werefurther classified as CD8 or CD4 T-cells if they expressed CD8 (CD8A orCD8B) or CD4, respectively. T-cells that expressed both CD4 and CD8 wereconsidered as unresolved. As a result, 67.3% of the cells had an initialcell-type assignment.

Next, Applicants clustered the cells with the Infomap algorithm (12).Infomap decomposes an input graph into modules by deriving a compressivedescription of random walks on the graph. The input to the algorithm wasan unweighted k-NN graph (k=50) that Applicants generated based on theexpression of the 194 biomarker genes across the CD45⁺ cells. Infomapproduced 22 clusters, separating the different CD45⁺ cells not onlyaccording to cell types but also according to various cell states. Foreach cluster, Applicants examined if it was enriched with cells of aspecific cell type, according to the initial assignments. Nineteenclusters were enriched with only one cell type. The cells within theseclusters were assigned to the cell type of their cluster, unless theirinitial assignment was different, and in this case, they were consideredas unresolved.

The cell-type assignments that were obtained by the two approaches werehighly concordant: 97% of the cells had the same assignment with bothapproaches.

Data-Driven Signatures of Specific Cell-Types

To identify cell-type signatures Applicants performed pairwisecomparisons between the nine different cell types that Applicantsidentified: malignant cells, CD8 and CD4 T-cells, NK cells, B-cells,macrophages, neutrophils, CAFs, and endothelial cells. Applicants thenperformed pairwise comparisons between the different cell types viaone-sided Wilcoxon ranksum-tests on the imputed and normalized data E′(see Data imputation and normalization). Genes that were overexpressedin a particular cell subtype compared to all other cell subtypes(Wilcoxon ranksum-test p-value <10⁻⁵) were considered as cell-typespecific. For cell types with less than 1,000 cells Applicants alsoranked the genes based on the maximal p-value that was obtained whencomparing the cell type to each of the other cell types; the bottom 100genes that also passed the first filter were considered as cell typespecific. As CD8 T-cells and NK cells had similar expression patterns,Applicants excluded NK cells from the analysis when identifying T-cellspecific genes. In the analyses described above Applicants consideredthe CD4 and CD8 as one entity of T-cells, but also derived CD4 and CD8specific signatures, by considering as separated entities. The lists ofcell-type specific genes are provided in table S4.

Differential Expression Between TN and ICR

To identify potential signatures of resistance, Applicants searched fortranscriptional features that distinguish between the cells of TN andICR patients, for each cell category separately. Applicants analyzedeach cell type that had a sufficient number (>100) of cells: malignantcells, macrophages, B cells, CD8 and CD4 T cells.

Applicants used sampling to mitigate the effects of outliers and preventtumors with a particularly large number of cells of a given cell typefrom dominating the results. In each sampling, Applicants selected asubset of the tumors, subsampled at most 30 cells of the given type fromeach tumor, and identified differentially expressed genes between theICR and TN cells. Differentially expressed genes were identified byapplying SCDE (13), a Bayesian method that was specifically developed todetect single-cell differential expression. As input to SCDE Applicantsused the normalized and imputed expression matrix E′ (see Dataimputation and normalization).

Applicants repeated the sampling procedure 500 times, and computed foreach gene g the fraction of subsamples in which it was found to besignificantly under (F_(down,g)) or over (F_(up,g)) expressed in the ICRpopulation compared to the TN population (|z-score|>1.96). Genes withF_(down,g) values larger than the 0.9 quantile of the F_(down)distribution were considered as potentially down-regulated in therespective ICR population. Likewise, genes with F_(up,g) values largerthan the 0.9 quantile were considered as potentially up-regulated in therespective ICR population.

Applicants further filtered the signatures with two additionalstatistical tests that Applicants applied on the full scRNA-seq data(E′) of the respective cell type (6). The first test was SCDE followedby multiple hypotheses correction (Holm-Bonferroni (14)). The second wasa non-parametric empirical test, where Applicants performed a one-sidedWilcoxon ranksum test to examine if a given gene is differentiallyexpressed in the ICR vs. TN cells. Applicants used E′ and not the rawcounts or log transformed TPM, as non-ordinal values violate theWilcoxon ranksum assumptions. Applicants corrected for multiplehypotheses testing using the Benjamini & Hochberg approach (15), andobtained empirical p-values to ensure the differences in expression werenot merely reflecting differences in cell quality (i.e., the number ofaligned reads per cell). To this end Applicants generated 1,000 randompermutations of the gene expression matrix E′, such that eachpermutation preserves the overall distribution of each gene, as well asthe association between the expression of each gene and cell quality.Applicants performed the Wilcoxon ranksum test on the permuted E′matrixes to compute the empirical p-values.

To assemble the final signatures, Applicants selected genes thatfulfilled the subsampling criteria described above and were mostsignificantly differentially expressed according to both the SCDE andempirical tests (top 200 genes with corrected P<0.05).

Mixed Effect Model for Testing the Differential Expression of GeneSignatures

To test the ability of a given gene signature to distinguish between theICR and TN patients Applicants modeled the data with a mixed-effectsmodel that accounts for the dependencies and structure in the data.Applicants used a hierarchical linear model (HLM) with two levels: (1)cell-level, and (2) sample-level. The sample-level controlled for thedependency between the scRNA-seq profiles of cells that were obtainedfrom the same patient, having a sample-specific intercept. The model hadoverall five covariates. Level-1 covariates were the number of reads(log-transformed) and the number of genes that were detected in therespective cell. Level-2 covariates were the patient's gender, age, andtreatment group, and a binary covariate that denotes if the sample was ametastatic or primary lesion. In the analyses of malignant cells,Applicants added another level-1 covariate that denoted which cellswhere cycling, based on the bimodal OE of the cell cycle signaturesdefined in (1) (see section Overall Expression (OE) of gene signatures).

To examine if a given signature was differentially expressed in the ICRcompared to the TN group Applicants used the HLM model to quantify thesignificance of the association between each of the model covariates andthe OE of the signature across the cells. Applicants applied thisapproach to examine the association between the treatment and the OE ofthe ICR and exclusion signatures. Applicants also tested annotated genesets that Applicants downloaded from MSigDB v6.0 (16) to examine ifcertain pre-defined pathways and biological functions weredifferentially expressed in the ICR vs. TN cells (table S9, FIG. 2C).

Applicants implemented the HLM model in R, using the lme4 and lmerTestpackages (CRAN.R-project.org/package=lme4,CRAN.R-project.org/package=lmerTest).

Cross Validation Analysis

To examine the generalizability of the oncogenic-ICR (mICR) signaturesApplicants performed a cross-validation procedure. In eachcross-validation round the test set consisted of all the cells of onepatient, and the training set consisted of the data from all the otherpatients in the cohort. In each round Applicants used only the trainingdata to generate mICR signatures (as described in Differentialexpression between TN and ICR), and computed the OE of the resultingmICR signatures in the cells of the test patient to obtain theirresistance scores (mICR-up minus mICR-down). To center the expressionmatrix for the computation of the OE values, Applicants used all themalignant cells in the data, such that the resistance scores of onepatient were relative to those of the other patients.

Integrating the Exclusion and Post-Treatment Programs

Applicants combined the post-treatment and exclusion programs with asimple union of the matching signatures, into the immune resistance geneprogram (Table S5). Applicants further refined the immune resistanceprogram by integrating the scRNA-seq data with the results of agenome-scale CRISPR screen that identified gene KOs which sensitizemalignant melanoma cells to T cell killing (Patel et al., 2017).Applicants defined our single malignant cells as putatively “resistant”if they underexpressed (lowest 1%) of one of the top hits of the screen:B2M, CD58, HLA-A, MLANA, SOX10, SRP54, TAP2, TAPBP. This underexpressiondid not reflect low cell quality, because these “resistant” cells had ahigher number of genes and reads. These cells had significantly higherimmune resistance scores (P=2.24*10⁻¹⁸ and 1.59*10⁻¹³, t-test and mixedeffects, respectively), and were enriched with cycling cells(P=1.74*10⁻¹³, hypergeometric test). Applicants identified the topmostdifferentially expressed genes by comparing the “resistant” cells toother malignant cells, and included in the refined immune resistance-up(down) signature only 25 (35) immune resistance-up (down) genes thatpass this additional differential expression test.

Applicants report the performances of all the resistance programsubsets: exclusion, post-treatment, and their union (Figures S4-S5). Ascomparators, Applicants used the hits of the co-culture screen alongwith other potentially prognostic signatures, to generate competingpredictors of patient survival and response (FIGS. 5E,H, Table S7, seesection Competing ICI response predictors).

T Cell Cytotoxicity and Exhaustion Signature Analysis

The analysis of T-cell exhaustion vs. T-cell cytotoxicity was performedas previously described (5), with six different exhaustion signatures,as provided in (1) and (17). First, Applicants computed the cytotoxicityand exhaustion scores of each CD8 T cell. Next, to control for theassociation between the expression of exhaustion and cytotoxicitymarkers, Applicants estimated the relationship between the cytotoxicityand exhaustion scores using locally-weighted polynomial regression(LOWESS, black line in FIG. 1E and FIG. S4B). Based on these values,Applicants defined T cells as functional if they fulfilled two criteria:(1) their cytotoxicity score was at the top 20% of the CD8 T cellpopulation (across all patients), and (2) their exhaustion scores werelower than expected given their cytotoxicity scores (below the dashedline in FIG. 1E and FIG. 4SB). Applicants then applied a hypergeometrictest to examine if the CD8 T cells of a given patient were enriched withfunctional cells.

Identifying T Cell Clones and Estimating the Fraction of ClonallyExpanded T-Cells

Applicants reconstructed the T-cell Receptors (TCRs) using TraCeR (18),with the Python package provided in github.com/Teichlab/tracer. TCRreconstruction significantly improved in the new cohort compared topreviously analyzed patients (table S1): 92% CD8 T-cells hadreconstructed TCRs, compared to only 50% such cells in the previouslypublished cohort (FIG. 9A). This is likely due to shorter read lengthand lower sequencing depth in the previous study (1). Applicantsassigned T cells to the clones defined in the TraCeR output.Reassuringly, cells from different patients were never falsely assignedto the same clone, and CD8 and CD4 T-cells were always assigned todifferent clones, even when they were obtained from the same tumor. Inthe CD8 T-cells Applicants detected 137 clones (FIG. 1F). In the CD4T-cells Applicants detected only 29 clones, with at most 3 cells perclone.

The size and number of clones that Applicants identified in each tumoris affected by the number of T-cells that were sequenced from thattumor, and the success rate of TCR reconstruction. To estimate thefraction of clonally expanded T-cells in a given tumor Applicantstherefore sampled its T-cells as follows. First, Applicants restrictedthe analysis to tumors with at least 20 CD8 T-cells with a full-lengthreconstructed TCR. Next, Applicants repeatedly sampled 20 cells fromeach tumor, such that, in each iteration, Applicants computed for everytumor the fraction of clonally expanded cells, namely, the fraction ofsampled cells that shared their TCR with another cell within the sampledpopulation. The average fraction of clonally expanded cells was used asan estimate of the T-cell clonal expansion level (FIG. 9B).

Cell Cycle Analysis

Applicants performed the following analysis to identify gene modulesthat characterize cycling cells specifically in CD8 T-cells (table S8).First, Applicants identified cycling cells in the CD8 T-cells and in themalignant cells based on the bimodal OE of a cell-cycle signature (theGO gene set cell cycle process, as defined in the Overall Expression(OE) of gene signatures section). Applicants then identifieddifferentially expressed genes (with SCDE (13)) between the cycling andnon-cycling cells, separately in the CD8 T-cells and in the malignantcells. Lastly, Applicants filtered from the resulting CD8 T-cell cyclingsignatures the genes that were also included in the correspondingmalignant signatures.

Characterizing Malignant Cells in Non-Infiltrated Tumors

Applicants developed an approach that combines scRNA-seq and bulkRNA-seq data to characterizes the state of a specific cell type intumors with a specific cell-type composition. Applicants applied it toidentify oncogenic programs that are induced or repressed in malignantcells that reside in tumors or niches with low T-cell infiltrationlevels. For clarity, Applicants describe the approach for this specificapplication, but note that it can also be applied in various othersettings, as long as the tumor composition can be well-defined, and thecell type of interest is adequately represented in the single cell data.

Applicants implemented the following step-wise approach (FIG. 2F):

1. Applicants provided as input the signature of malignant cells and thesignature of T-cells, defined above (section: Cell-type specificsignatures).2. Applicants obtained the bulk RNA-Seq data of 473 Skin CutaneousMelanoma (SKCM) tumors from TCGA (as provided inxenabrowser.net/datapages/). On this bulk data Applicants (a) estimatedof the T-cell infiltration level in each tumor by computing the OE ofthe T-cell signature in each of the bulk samples; (b) computed theSpearman correlation coefficient between the expression of each of thegenes in the malignant signature and the OE of the T-cell signatureacross the 473 bulk tumors; and (c) defined the seed exclusion-up (down)signature as the top 20 malignant genes that were significantlynegatively (positively) correlated in (b).3. Applicants analyzed the scRNA-Seq data of the malignant cells in thefollowing way. (a) Applicants computed the OE of the seed T_(exc)signatures in each of the malignant cell profiles; (b) Applicantscomputed the partial Spearman correlation coefficient between theexpression of each gene and the OE of the seed T_(exc) signatures acrossthe single malignant cells, while controlling for technical quality (thenumber of reads and genes that were detected in the cells).4. Applicants derived the final genome-scale exclusion signatures,defined as: (i) exclusion-up: Genes which are significantly positivelycorrelated with the seed T_(exc)-up signature and significantlynegatively correlated with the seed T_(exc)-down signature; and (ii)exclusion_(c)-down: Genes which are significantly positively correlatedwith the seed T_(exc)-down signature and significantly negativelycorrelated with the seed exclusion-up signature. In this analysis, agene was defined as significantly correlated with a signature if it wasamong the 200 topmost correlated genes, with P-value <10⁻¹⁰, and|R|>0.1.

Integration of the Exclusion and Oncogenic-ICR Signatures

Applicants combined the mICR and T_(exc) signatures with a simple unionof the matching signatures, into the uICR gene signatures. Applicantsfurther refined the uICR signatures by identifying putative “resistant”malignant cells as those that under-expressed (lowest 1%) one of the tophits of a CRISPR screen (19) in malignant melanoma for resistance toT-cell killing: B2M, CD58, HLA-A, MLANA, SOX10, SRP54, TAP2, TAPBP.(This under-expression did not reflect low cell quality, because these“resistant” cells had a higher number of genes and reads. These cellshad significantly higher uICR scores (P=2.24*10⁻¹⁸ and 1.59*10⁻³, t-testand mixed effects, respectively), and were enriched with cycling cells(P=1.74*10⁻¹³, hypergeometric test).) Applicants the topmostdifferentially expressed genes by comparing the “resistant” cells toother malignant cells (13), and included in the refined uICR-up (down)signature only 25 (35) uICR-up (down) genes that pass this additionaldifferential expression test.

Applicants report the performances of all the resistance signatures:oncogenic-ICR, exclusion, and their union (uICR), with and without thisadditional refinement (FIGS. 11-13). For comparison, Applicants used thehits of the co-culture screen along with other potentially prognosticsignatures, to generate competing predictors of patient survival andresponse (FIG. 4, E,H, tables S10, see section Competing ICRpredictors).

Cell-Cell Interaction Network

Applicants generated genome-scale cell-cell interactions networks byintegrating (1) protein-protein interactions that were previouslyassembled by (20) as cognate ligand-receptor pairs, with (2)cell-subtype specific signatures from the single cell profiles,identified as described above. The resulting network maps the physicalinteractions between the different cell subtypes that Applicantscharacterized. Each cell subtype and protein are represented by a nodein the network. An edge between a cell subtype node and a ligand orreceptor node denotes that this protein is included in the cell subtypesignatures. An edge between two proteins denotes that they canphysically bind to each other and mediate cell-cell interactions. A pathfrom one cell subtype to another represents a potential route by whichthe cells can interact. For each cell subtype, Applicants defined a‘communication signature’, which includes all the surface proteins thatbind to the cell subtype signature proteins. To examine if the ICRmalignant cells suppress their interactions with other cell subtypesApplicants examined if the different oncogenic resistance signatureswere enriched (hypergeometric test) with genes from the different immuneand stroma ‘communication signatures’ (FIG. 3E). An interactive map ofthe cell-cell interaction network is provided as supplementary files,and can be explored with Cytoscape (21) provided in www.cytoscape.org.

Survival and ICI-Response Predictions

To test if a given signature can predict survival or progressionfree-survival (PFS) Applicants first computed the OE of the signature inbulk RNA-Seq in each patient tumor. Next, Applicants used a Coxregression model with censored data to compute the association and itssignificance. To examine if the signature's predictive value wassignificant beyond T-cell infiltration levels Applicants computed foreach sample the OE of the T-cell signature (above), used this as anothercovariate in the Cox regression model, and computed another p-value forthe given signature, based on its association with survival or PFS inthis two-covariate model.

To visualize the predictions of a specific signature in a Kaplan Meier(KM) plot, Applicants stratified the patients into three groupsaccording to the OE of the signature: high or low expression correspondto the top or bottom 25% of the population, respectively; intermediateexpression is between the upper and lower quartiles (26%-74%,interquartile range). Applicants used a one-sided logrank test toexamine if there was a significant difference between these threepatient groups in terms of their survival or PFS rates.

CB was defined according to the RESICT criteria, such that patients witha complete or partial response were defined as CB patients. Patientswith progressive disease were defined as non-CB, and patients with moreill-defined response, as stable disease or marginal responses wereexcluded from this analysis. Applicants further stratified the CBpatients according to the duration of the response: (1) less than 6months, (2) more than 6 months and less than a year, and (3) more than ayear (long-term CB). Applicants then applied one-sided t-tests toexamine if the OE of the different signatures were differentiallyexpressed in the CB vs. non-CB patients, or in the long-term CB patientscompared to the non-CB patients. Finally, Applicants tested the abilityof the different signatures to predict complete response by comparing(t-test) between the complete responders and the all other patients witha RECIST annotation (n=101, FIG. 411 and FIG. 14), and computing theArea Under the Curve (AUC) of the resulting Receiver OperatingCharacteristic (ROC) curve.

Multiplexed, Tissue Cyclic Immunofluorescence (t-CyCIF) of FFPE TissueSlides

Formalin-fixed, paraffin-embedded (FFPE) tissue slides, 5 μm inthickness, were generated at the Brigham and Women's Hospital PathologyCore Facility from tissue blocks collected from patients underIRB-approved protocols (DFCI 11-104). Multiplexed, tissue cyclicimmunofluorescence (t-CyCIF) was performed as described recently (Lin etal., 2017). For direct immunofluorescence, Applicants used the followingantibodies: CEP170 (Abcam, ab84545), LAMP2 (R&D technologies, AF6228),MITF (Abcam, ab3201), DLL3 (Abcam, ab103102, Rab), MITF (Abcam, ab3201,Ms), S100α-488 (Abcam, ab207367), CD3-555 (Abcam, ab208514), CD8a-660(eBioscience, 50-0008-80), cJUN-488 (Abcam, ab193780), cMyc-555 (Abcam,ab201780), HLAA-647 (Abcam, ab199837), TP53-488 (Cell Signaling, 5429),SQSTM1-555 (Abcam, ab203430). Stained slides from each round of CycIFwere imaged with a CyteFinder slide scanning fluorescence microscope(RareCyte Inc. Seattle Wash.) using either a 10× (NA=0.3) or 40×long-working distance objective (NA=0.6). Imager5 software (RareCyteInc.) was used to sequentially scan the region of interest in 4fluorescence channels. Image processing, background subtraction, imageregistration, single-cell segmentation and quantification were performedas previously described (Lin et al., 2017).

Mapping Cell-Cell Interactions Based on Imaging Data

Given the processed imaging data, Applicants assigned cells into celltypes by discretizing the log-transformed expression levels of the celltype markers (S100, MITF, CD3, and CD8). Applicants applied the EMalgorithm for mixtures of normal distributions to characterize the twonormal distributions for each of these cell type marker intensities.S100⁺/MITF⁺/CD3⁻/CD8⁻ cells were defined as malignant cells;S100⁻/MITF⁻/CD3⁺/CD8⁻ cells were defined as T cells, andS100⁻/MITF⁻/CD3⁺/CD8⁺ cells were defined as CD8 T cells; other cellswere defined as uncharacterized.

For each image Applicants constructed a Delaunay (Gabriel) graph, wheretwo cells are connected to each other if there is no other cell betweenthem. Following the approach presented in (Goltsev et al., 2017),Applicants examined if cells of certain types were less/more likely tobe connected to each other in the graph. To this end, Applicantscomputed the odds ratio of cell-cell interactions of cell type A andcell type B by computing the observed frequency of interactions dividedby the expected theoretical frequency (calculated as the total frequencyof edges incident to type A multiplied by the total frequency of edgesincident to type B). Two cell types are less or more likely to interactthan expected by chance if the log-transformed odds ratio is less ormore than 0, respectively. The significance of the deviation from zerowas tested using the binomial distribution test.

Next, Applicants examined the association between the expression of thedifferent markers in the malignant cells and the level of T cellinfiltration. Each image in our data was composed of a few hundredframes (119-648 frames/image), where each frame consists of 1,377 cellson average. In each frame, Applicants computed the fraction of T cellsand the average expression of the different markers in the malignantcells. Applicants then used a hierarchical logistic regression model toquantify the associations. The independent variables included theaverage expression of the marker in the malignant cells of the frame(level-1), the average expression of normalization markers in themalignant cells of the frame (level-1), and the image the frame wassampled from (level-2). The dependent variable was the discretized Tcell infiltration level of the frame, defining frames with high/lowlymphocyte-fraction as “hot”/“cold”, respectively. Applicants useddifferent cutoffs to define hot/cold frames, such that a frame with a Tcell fraction <Q was defined as cold. Applicants report the results thatwere consistent across multiple definitions of Q, and provide thep-value obtained with Q=the median T cell fraction across all framesfrom all images.

Integrating scRNA-Seq and Spatial Data

Applicants integrated the scRNA-seq and multiplexed immunofluorescence(t-CyCIF) data via a variant of Canonical Correlation Analysis (CCA),using the code provided in the R toolkit Seurat (Butler and Satija,2017). CCA aims to identify shared correlation structures acrossdatasets, such that each dataset provides multiple measurements of agene-gene covariance structure, and patterns which are common to bothdatasets are identified. Cells from both sources are then represented inan aligned-CCA space (Butler and Satija, 2017).

In our application, each cell in the t-CyCIF data was represented by thelog-transformed intensities of 14 markers. Each cell in the scRNA-seqdata was represented by the imputed expression of the genes encoding thesame 14 proteins. To impute the scRNA-seq data Applicants identified asignature for each marker, consisting of the top 50 genes which weremostly correlated with the marker expression across the cell populationin the scRNA-seq data. Applicants then used the OE of the markersignature as a measure of its activity in the scRNA-seq data.

The cells from both sources were represented in the resultingaligned-CCA space. Next, Applicants used the first five aligned-CCAdimensions to cluster the cells and represented them in a 2D t-SNEembedding (Laurens Maaten, 2009). Clustering was preformed using ashared nearest neighbor (SNN) modularity optimization based clusteringalgorithm, which calculates k-nearest neighbors, constructs an SNNgraph, and optimizes the modularity function to determine clusters(Waltman and van Eck, 2013).

To examine if cells clustered according to cell type or according tosource Applicants computed the expected number of cells from each twocategories to be assigned to the same cluster by chance, assuming arandom distribution of cells into clusters. Applicants then used theobserved vs. expected co-clustering ratio to quantify the deviation fromthe random distribution, and used the binomial test to compute thestatistical significance of this deviation from random.

Survival and ICI-Response Predictions

To test if a given signature predicts survival or progressionfree-survival (PFS) Applicants first computed the OE of the signature ineach tumor based on the bulk RNA-Seq data. Next, Applicants used a Coxregression model with censored data to compute the significance of theassociation between the OE values and prognosis. To examine if thesignature's predictive value was significant beyond T cell infiltrationlevels Applicants computed for each sample the OE of our T cellsignature (above), used this as another covariate in the Cox regressionmodel, and computed another p-value for each signature, based on itsassociation with survival or PFS in this two-covariate model.

To visualize the predictions of a specific signature in a Kaplan Meier(KM) plot, Applicants stratified the patients into three groupsaccording to the OE of the signature: high or low expression correspondto the top or bottom 25% of the population, respectively, andintermediate otherwise. Applicants used a one-sided log-rank test toexamine if there was a significant difference between these threepatient groups in terms of their survival or PFS rates.

CB was defined according to RESICT criteria, such that patients with acomplete or partial response were defined as CB patients. Patients withprogressive disease were defined as non-CB, and patients with moreill-defined response, such as stable disease or marginal responses, wereexcluded from this analysis. Applicants further stratified the CBpatients according to the duration of the response: (1) less than 6months, (2) more than 6 months and less than a year, and (3) more than ayear (long-term CB). Applicants applied one-sided t-tests to examine ifthe OE of the different signatures were differentially expressed in theCB vs. non-CB patients, or in the long-term CB patients compared to thenon-CB patients. Finally, Applicants tested the ability of the differentsignatures to predict complete response by comparing (t-test) betweenthe complete responders and all other patients with a RECIST annotation(n=101, FIGS. 5H and S5F), and computing the Area Under the Curve (AUC)of the resulting ROC curve.

Controlling for Cell Cycle Effects in the Resistance OE Scores

The single-cell data demonstrated that cycling cells have higherexpression of resistance states, according to the oncogenic-ICR,exclusion, and uICR signatures. Since the tumor proliferation rate maybe a dynamic and context-dependent property, it might be advisable tocompare between tumors based on their basal resistance level, namely,after controlling for the cell cycle effect. To this end, Applicantscompute for each tumor the OE of two cell cycle signatures (G1/S andG2/M signatures in table S10). Applicants then fitted a linear model toestimate the expected OE of the resistance signature, when using the OEof the two cell cycle signatures as covariates. The residuals of thislinear model, which quantify the deviation from the expected resistanceOE values, were considered as the basal resistance level. Applicantspreformed this analysis with different resistance signatures (e.g.,uICR, exclusion, etc.).

Alternative ICR Predictors

To compare the predictive value of the resistance signature to that ofother signatures, Applicants repeated the prediction process, asdescribe in Survival and ICI-response predictions, for each of thefollowing gene signatures (table S10): (1) Cell-type specific signaturesidentified from the scRNA-Seq (as described in the Cell-type specificsignatures section); (2) Signatures that characterize oncogenic cellstates in melanoma (the AXL-high, MITF-high, and cell cycle states from(1)); (3) Six different sets of genes whose guides were found to bedifferentially (FDR <0.05) depleted or enriched in the in vivo CRISPRscreen of (22), designed to identify key regulators of immune evasionand ICR in melanoma, based on the pairwise comparisons of threeexperimental settings. The data was obtained from Table S1 of (22); (4)The genes whose guides were most preferentially and significantlyenriched (top 10 and top 50) in the co-culture conditions in thegenome-scale CRISPR screen of (19), also designed to identify keyregulators of immune evasion and ICR in melanoma; (5) Immune-relatedsignatures that were identified based on the analysis of multiplePembrolizumab clinical datasets, and were shown to predict the responseto Pembrolizumab in an independent cohort (23); (6) The FluidigmAdvanta™ Immuno-Oncology Gene Expression signatures(www.fluidigm.com/applications/advanta-immuno-oncology-gene-expression-assay);and (7) PDL1 expression.

Applicants summarize in table S10 the predictive value of each of thesesignatures when applied to predict melanoma (TCGA) patient survival, andthe PFS, clinical benefit (CB), and complete response in the melanomapatients of the aPD1 cohort.

Comparison of Pre- and Post-Treatment Samples in Validation Cohort 2

Applicants used a mixed-effects model to represent the data and examinethe association between the expression of various gene signatures anddifferent treatment categories. The model included two levels. Thefirst, sample-level, had 12 covariates, the first three denote whetherthe sample was exposed to: (1) targeted therapy (on/postRAF/MEK-inhibitors), (2) ICI (on/post), with or without an additionalimmunotherapy, (3) non-ICI immunotherapy (NK antibodies, IL2, IFN, or GMCSF) without ICI. The other 9 sample-level covariates control forpotential changes in the tumor microenvironment by providing the OE ofthe different non-malignant cell subtype signatures that Applicantsidentified (table S4). The second, patient-level, controlled for thedependency between the scRNA-seq profiles of samples that were obtainedfrom the same patient, having a patient-specific intercept that providedthe baseline level for each patient.

Applicants used the mixed effects model to quantify the associationbetween the different ICR signatures and the exposure to ICI or targetedtherapy (the second and first sample-level covariates, respectively).When testing the association between the tumor composition and thetreatments Applicants used the model described above without the 9 TMEcovariates.

Applicants implemented the HLM model in R, using the lme4 and lmerTestpackages (CRAN.R-project.org/package=lme4,CRAN.R-project.org/package=lmerTest).

For each resistance signature, Applicants applied ANOVA to test if theinter-patient variation in the OE values was significantly greater thanthe intra-patient variation, and reported the least significant ANOVAp-value that was obtained.

Searching for Immune Sensitizing Drugs

Applicants performed the following analysis to identify drugs that couldselectively eradicate malignant cells with a high expression of theresistance program, using efficacy measures of 131 drugs across 639human cancer cell lines (Garnett et al., 2012). For each drug,Applicants defined sensitive cell lines as those with the lowest (bottom10%) IC50 values. Applicants then used the gene expression provided in(Garnett et al., 2012), computed the OE of the resistance program ineach of the 639 cells, and defined “resistant” cell lines as those withthe highest OE values (top 10%). Next, for each drug Applicants built ahierarchical logistic regression model, where the dependent variable isthe cell line's (drug-specific) binary sensitivity assignment, and theindependent variables are the cell lines' “resistance” assignments(level-1) and cancer types (level-2). Drugs then were ranked based onthe one-tailed p-values that quantify the significance of the positiveassociation between the drug sensitivity (dependent) variable and theimmune resistance (independent) variable.

Abemaciclib Treatment of Melanoma Cell Lines

Established melanoma cell lines IGR39, UACC62 and A2058 were acquiredfrom the Cancer Cell Line Encyplopedia (CCLE) from the Broad Institute.Cells were treated every 3 days with 500 nM abemacilib (LY2835219,MedChemExpress) or DMSO control. The doubling time of each cell line wasestablished and lines were seeded such that cells collected forscRNA-seq were derived from culture dishes with ˜50-60% confluency onday 7 of treatment. Cells were lifted of culture dishes using Versenesolution (Life Technologies), washed twice in 1×PBS, counted andresuspended in PBS supplemented with 0.04% BSA for loading for scRNA-seqwith the 10× Genomics platform.

Melanoma-TIL Co-Culture

An autologous pair of melanoma and TIL culture was provided by MDAnderson Cancer Center and were established using previously describedprotocols (Peng et al., 2016). Melanoma cells were pre-treated with 500nM abemaciclib or DMSO control for 7 days followed by co-culture withautologous TILs (with an effector to target ratio of 5:1) for 48 hours.TILs were removed by pipetting of the supernatant, and the remainingmelanoma cells were washed twice with PBS, lifted off the culture dish,and resuspended in PBS supplemented with 0.04% BSA for loading forscRNA-seq with the 10× Genomics platform.

Data Availability

Processed scRNA-seq data generated for this study is currently providedthrough the single-cell portal in a ‘private’ mode. To access the datalogin to the portal (portals.broadinstitute.org/single_cell) via theemail account icr.review1@gmail.com, with the password icrreview1, anduse the following link to view or download the dataportals.broadinstitute.org/single_cell/study/melanoma-immunotherapy-resistance.The processed data will also be available through the Gene ExpressionOmnibus (GEO), and raw scRNA-seq data will be deposited in dbGAP.

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Various modifications and variations of the described methods,pharmaceutical compositions, and kits of the invention will be apparentto those skilled in the art without departing from the scope and spiritof the invention. Although the invention has been described inconnection with specific embodiments, it will be understood that it iscapable of further modifications and that the invention as claimedshould not be unduly limited to such specific embodiments. Indeed,various modifications of the described modes for carrying out theinvention that are obvious to those skilled in the art are intended tobe within the scope of the invention. This application is intended tocover any variations, uses, or adaptations of the invention following,in general, the principles of the invention and including suchdepartures from the present disclosure come within known customarypractice within the art to which the invention pertains and may beapplied to the essential features herein before set forth.

1. A method of detecting an immune checkpoint inhibitor resistance (ICR)gene signature in a tumor comprising, detecting in tumor cells obtainedfrom a subject in need thereof the expression or activity of a malignantcell gene signature comprising: a) one or more genes or polypeptidesselected from the group consisting of C1QBP, CCT2, CCT6A, DCAF13,EIF4A1, ILF2, MAGEA4, NONO, PA2G4, PGAM1, PPA1, PPIA, RPL18A, RPL26,RPL31, RPS11, RPS15, RPS21, RPS5, RUVBL2, SAE1, SNRPE, UBA52, UQCRH,VDAC2, AEBP1, AHNAK, APOC2, APOD, APOE, B2M, C10orf54, CD63, CTSD, EEA1,EMP1, FBXO32, FYB, GATSL3, HCP5, HLA-A, HLA-B, HLA-C, HLA-E, HLA-F,HLA-H, ITGA3, LAMP2, LYRM9, MFGE8, MIA, NPC2, NSG1, PROS1, RDH5,SERPINA1, TAPBP, TIMP2, TNFSF4 and TRIML2; or b) one or more genes orpolypeptides selected from the group consisting of ACAT1, ACP5, ACTB,ACTG1, ADSL, AEN, AK2, ANP32E, APP, ASAP1, ATP5A1, ATP5D, ATP5G2, BANCR,BCAN, BZW2, C17orf76-AS1, C1QBP, C20orf112, C6orf48, CA14, CBX5, CCT2,CCT3, CCT6A, CDK4, CEP170, CFL1, CHP1, CNRIP1, CRABP2, CS, CTPS1, CYC1,DAP3, DCAF13, DCT, DDX21, DDX39B, DLL3, EDNRB, EEF1D, EEF1G, EEF2,EIF1AX, EIF2S3, EIF3E, EIF3K, EIF3L, EIF4A1, EIF4EBP2, ESRP1, FAM174B,FAM178B, FAM92A1, FBL, FBLN1, FOXRED2, FTL, FUS, GABARAP, GAS5, GNB2L1,GPATCH4, GPI, GRWD1, GSTO1, H3F3A, H3F3AP4, HMGA1, HNRNPA1, HNRNPA1P10,HNRNPC, HSPA8, IDH2, IFI16, ILF2, IMPDH2, ISYNA1, ITM2C, KIAA0101,LHFPL3-AS1, LOC100190986, LYPLA1, MAGEA4, MARCKS, MDH2, METAP2, MID1,MIR4461, MLLT11, MPZL1, MRPL37, MRPS12, MRPS21, MYC, NACA, NCL, NDUFS2,NF2, NID1, NOLC1, NONO, NPM1, NUCKS1, OAT, PA2G4, PABPC1, PAFAH1B3,PAICS, PFDN2, PFN1, PGAM1, PIH1D1, PLTP, PPA1, PPIA, PPP2R1A, PSAT1,PSMD4, PTMA, PYCARD, RAN, RASA3, RBM34, RNF2, RPAIN, RPL10, RPL10A,RPL11, RPL12, RPL13, RPL13A, RPL13AP5, RPL14, RPL17, RPL18, RPL18A,RPL21, RPL26, RPL28, RPL29, RPL3, RPL30, RPL31, RPL35, RPL36A, RPL37,RPL37A, RPL39, RPL4, RPL41, RPL5, RPL6, RPL7, RPL7A, RPL8, RPLP0, RPLP1,RPS10, RPS11, RPS12, RPS15, RPS15A, RPS16, RPS17, RPS17L, RPS18, RPS19,RPS2, RPS21, RPS23, RPS24, RPS26, RPS27, RPS27A, RPS3, RPS3A, RPS4X,RPS5, RPS6, RPS7, RPS8, RPS9, RPSA, RSL1D1, RUVBL2, SAE1, SCD, SCNM1,SERBP1, SERPINF1, SET, SF3B4, SHMT2, SKP2, SLC19A1, SLC25A3, SLC25A5,SLC25A6, SMS, SNAI2, SNHG16, SNHG6, SNRPE, SORD, SOX4, SRP14, SSR2,TIMM13, TIMM50, TMC6, TOP1MT, TP53, TRAP1, TRPM1, TSR1, TUBA1B, TUBB,TUBB4A, TULP4, TXLNA, TYRP1, UBA52, UCK2, UQCRFS1, UQCRH, USP22, VCY1B,VDAC2, VPS72, YWHAE, ZFAS1, ZNF286A, A2M, ACSL3, ACSL4, ADM, AEBP1, AGA,AHNAK, ANGPTL4, ANXA1, ANXA2, APLP2, APOC2, APOD, APOE, ARF5, ARL6IP5,ATF3, ATP1A1, ATP1B1, ATP1B3, ATRAID, B2M, BACE2, BBX, BCL6, C10orf54,C4A, CALU, CASP1, CAST, CAV1, CBLB, CCND3, CD151, CD44, CD47, CD58,CD59, CD63, CD9, CDH19, CHI3L1, CHN1, CLIC4, CLU, CPVL, CRELD1, CRYAB,CSGALNACT1, CSPG4, CST3, CTSA, CTSB, CTSD, CTSL1, DAG1, DCBLD2, DDR1,DDX5, DPYSL2, DSCR8, DUSP4, DUSP6, DYNLRB1, ECM1, EEA1, EGR1, EMP1,EPHX2, ERBB3, EVA1A, EZH1, EZR, FAM3C, FBXO32, FCGR2C, FCRLA, FGFR1,FLJ43663, FOS, FYB, GAA, GADD45B, GATSL3, GEM, GOLGB1, GPNMB, GRN, GSN,HCP5, HLA-A, HLA-B, HLA-C, HLA-E, HLA-F, HLA-H, HPCAL1, HSPA1A, HSPA1B,HTATIP2, ID2, IFI27L2, IFI35, IGF1R, IL1RAP, IL6ST, ISCU, ITGA3, ITGA6,ITGA7, ITGB1, ITGB3, ITM2B, JUN, KCNN4, KLF4, KLF6, KRT10, LAMP2,LEPROT, LGALS1, LGALS3, LGALS3BP, LOC100506190, LPL, LRPAP1, LTBP3,LYRM9, MAEL, MAGEC2, MAP1B, MATN2, MFGE8, MFI2, MIA, MRPS6, MT1E, MT1M,MT1X, MT2A, NDRG1, NEAT1, NFKBIA, NFKBIZ, NNMT, NPC1, NPC2, NR4A1, NSG1,OCIAD2, PAGES, PDK4, PERP, PKM, PLP2, PRKCDBP, PRNP, PROS1, PRSS23,PSAP, PSMB9, PTRF, RDH5, RNF145, RPS4Y1, S100A13, S100A6, S100B, SAT1,SCARB2, SCCPDH, SDC3, SEL1L, SEMA3B, SERPINA1, SERPINA3, SERPINE2, SGCE,SGK1, SLC20A1, SLC26A2, SLC39A14, SLC5A3, SNX9, SOD1, SPON2, SPRY2,SQSTM1, SRPX, STOM, SYNGR2, SYPL1, TAPBP, TAPBPL, TF, TGOLN2, THBD,TIMP1, TIMP2, TIMP3, TIPARP, TM4SF1, TMBIM6, TMED10, TMED9, TMEM66,TMX4, TNC, TNFSF4, TPP1, TRIML2, TSC22D3, TSPYL2, TXNIP, TYR, UBC, UPP1,XAGE1A, XAGE1B, XAGE1C, XAGE1D, XAGE1E, ZBTB20 and ZBTB38; or c) one ormore genes or polypeptides selected from the group consisting of ANP32E,CTPS1, DDX39B, EIF4A1, ESRP1, FBL, FUS, HNRNPA1, ILF2, KIAA0101, NUCKS1,PTMA, RPL21, RUVBL2, SET, SLC25A5, TP53, TUBA1B, UCK2, YWHAE, APLP2,ARL6IP5, CD63, CLU, CRELD1, CTSD, CTSL1, FOS, GAA, GRN, HLA-F, ITM2B,LAMP2, MAP1B, NPC2, PSAP, SCARB2, SDC3, SEL1L, TMED10 and TSC22D3; or d)one or more genes or polypeptides selected from the group consisting ofMT1E, MT1M, MT1X and MT2A.
 2. The method according to claim 1, whereinsaid ICR signature comprises a ICR-down signature, said signaturecomprising one or more genes selected from the group consisting of: a)AEBP1, AHNAK, APOC2, APOD, APOE, B2M, C10orf54, CD63, CTSD, EEA1, EMP1,FBXO32, FYB, GATSL3, HCP5, HLA-A, HLA-B, HLA-C, HLA-E, HLA-F, HLA-H,ITGA3, LAMP2, LYRM9, MFGE8, MIA, NPC2, NSG1, PROS1, RDH5, SERPINA1,TAPBP, TIMP2, TNFSF4 and TRIML2; or b) A2M, ACSL3, ACSL4, ADM, AEBP1,AGA, AHNAK, ANGPTL4, ANXA1, ANXA2, APLP2, APOC2, APOD, APOE, ARF5,ARL6IP5, ATF3, ATP1A1, ATP1B1, ATP1B3, ATRAID, B2M, BACE2, BBX, BCL6,C10orf54, C4A, CALU, CASP1, CAST, CAV1, CBLB, CCND3, CD151, CD44, CD47,CD58, CD59, CD63, CD9, CDH19, CHI3L1, CHN1, CLIC4, CLU, CPVL, CRELD1,CRYAB, CSGALNACT1, CSPG4, CST3, CTSA, CTSB, CTSD, CTSL1, DAG1, DCBLD2,DDR1, DDX5, DPYSL2, DSCR8, DUSP4, DUSP6, DYNLRB1, ECM1, EEA1, EGR1,EMP1, EPHX2, ERBB3, EVA1A, EZH1, EZR, FAM3C, FBXO32, FCGR2C, FCRLA,FGFR1, FLJ43663, FOS, FYB, GAA, GADD45B, GATSL3, GEM, GOLGB1, GPNMB,GRN, GSN, HCP5, HLA-A, HLA-B, HLA-C, HLA-E, HLA-F, HLA-H, HPCAL1,HSPA1A, HSPA1B, HTATIP2, ID2, IFI27L2, IFI35, IGF1R, IL1RAP, IL6ST,ISCU, ITGA3, ITGA6, ITGA7, ITGB1, ITGB3, ITM2B, JUN, KCNN4, KLF4, KLF6,KRT10, LAMP2, LEPROT, LGALS1, LGALS3, LGALS3BP, LOC100506190, LPL,LRPAP1, LTBP3, LYRM9, MAEL, MAGEC2, MAP1B, MATN2, MFGE8, MFI2, MIA,MRPS6, MT1E, MT1M, MT1X, MT2A, NDRG1, NEAT1, NFKBIA, NFKBIZ, NNMT, NPC1,NPC2, NR4A1, NSG1, OCIAD2, PAGES, PDK4, PERP, PKM, PLP2, PRKCDBP, PRNP,PROS1, PRSS23, PSAP, PSMB9, PTRF, RDH5, RNF145, RPS4Y1, S100A13, S100A6,S100B, SAT1, SCARB2, SCCPDH, SDC3, SEL1L, SEMA3B, SERPINA1, SERPINA3,SERPINE2, SGCE, SGK1, SLC20A1, SLC26A2, SLC39A14, SLC5A3, SNX9, SOD1,SPON2, SPRY2, SQSTM1, SRPX, STOM, SYNGR2, SYPL1, TAPBP, TAPBPL, TF,TGOLN2, THBD, TIMP1, TIMP2, TIMP3, TIPARP, TM4SF1, TMBIM6, TMED10,TMED9, TMEM66, TMX4, TNC, TNFSF4, TPP1, TRIML2, TSC22D3, TSPYL2, TXNIP,TYR, UBC, UPP1, XAGE1A, XAGE1B, XAGE1C, XAGE1D, XAGE1E, ZBTB20 andZBTB38; or c) APLP2, ARL6IP5, CD63, CLU, CRELD1, CTSD, CTSL1, FOS, GAA,GRN, HLA-F, ITM2B, LAMP2, MAP1B, NPC2, PSAP, SCARB2, SDC3, SEL1L, TMED10and TSC22D3, wherein said ICR-down signature is downregulated in a tumorwith a high ICR score and upregulated in a tumor with a low ICR score;and/or wherein said ICR signature comprises a ICR-up signature, saidsignature comprising one or more genes selected from the groupconsisting of: a) C1QBP, CCT2, CCT6A, DCAF13, EIF4A1, ILF2, MAGEA4,NONO, PA2G4, PGAM1, PPA1, PPIA, RPL18A, RPL26, RPL31, RPS11, RPS15,RPS21, RPS5, RUVBL2, SAE1, SNRPE, UBA52, UQCRH and VDAC2; or b) ACAT1,ACP5, ACTB, ACTG1, ADSL, AEN, AK2, ANP32E, APP, ASAP1, ATP5A1, ATP5D,ATP5G2, BANCR, BCAN, BZW2, C17orf76-AS1, C1QBP, C20orf112, C6orf48,CA14, CBX5, CCT2, CCT3, CCT6A, CDK4, CEP170, CFL1, CHP1, CNRIP1, CRABP2,CS, CTPS1, CYC1, DAP3, DCAF13, DCT, DDX21, DDX39B, DLL3, EDNRB, EEF1D,EEF1G, EEF2, EIF1AX, EIF2S3, EIF3E, EIF3K, EIF3L, EIF4A1, EIF4EBP2,ESRP1, FAM174B, FAM178B, FAM92A1, FBL, FBLN1, FOXRED2, FTL, FUS,GABARAP, GAS5, GNB2L1, GPATCH4, GPI, GRWD1, GSTO1, H3F3A, H3F3AP4,HMGA1, HNRNPA1, HNRNPA1P10, HNRNPC, HSPA8, IDH2, IFI16, ILF2, IMPDH2,ISYNA1, ITM2C, KIAA0101, LHFPL3-AS1, LOC100190986, LYPLA1, MAGEA4,MARCKS, MDH2, METAP2, MID1, MIR4461, MLLT11, MPZL1, MRPL37, MRPS12,MRPS21, MYC, NACA, NCL, NDUFS2, NF2, NID1, NOLC1, NONO, NPM1, NUCKS1,OAT, PA2G4, PABPC1, PAFAH1B3, PAICS, PFDN2, PFN1, PGAM1, PIH1D1, PLTP,PPA1, PPIA, PPP2R1A, PSAT1, PSMD4, PTMA, PYCARD, RAN, RASA3, RBM34,RNF2, RPAIN, RPL10, RPL10A, RPL11, RPL12, RPL13, RPL13A, RPL13AP5,RPL14, RPL17, RPL18, RPL18A, RPL21, RPL26, RPL28, RPL29, RPL3, RPL30,RPL31, RPL35, RPL36A, RPL37, RPL37A, RPL39, RPL4, RPL41, RPL5, RPL6,RPL7, RPL7A, RPL8, RPLP0, RPLP1, RPS10, RPS11, RPS12, RPS15, RPS15A,RPS16, RPS17, RPS17L, RPS18, RPS19, RPS2, RPS21, RPS23, RPS24, RPS26,RPS27, RPS27A, RPS3, RPS3A, RPS4X, RPS5, RPS6, RPS7, RPS8, RPS9, RPSA,RSL1D1, RUVBL2, SAE1, SCD, SCNM1, SERBP1, SERPINF1, SET, SF3B4, SHMT2,SKP2, SLC19A1, SLC25A3, SLC25A5, SLC25A6, SMS, SNAI2, SNHG16, SNHG6,SNRPE, SORD, SOX4, SRP14, SSR2, TIMM13, TIMM50, TMC6, TOP1MT, TP53,TRAP1, TRPM1, TSR1, TUBA1B, TUBB, TUBB4A, TULP4, TXLNA, TYRP1, UBA52,UCK2, UQCRFS1, UQCRH, USP22, VCY1B, VDAC2, VPS72, YWHAE, ZFAS1 andZNF286A, or c) ANP32E, CTPS1, DDX39B, EIF4A1, ESRP1, FBL, FUS, HNRNPA1,ILF2, KIAA0101, NUCKS1, PTMA, RPL21, RUVBL2, SET, SLC25A5, TP53, TUBA1B,UCK2 and YWHAE, wherein said ICR-up signature is upregulated in a tumorwith a high ICR score and downregulated in a tumor with a low ICR score.3. (canceled)
 4. A method of detecting an immune checkpoint inhibitorresistance (ICR) gene signature in a tumor comprising, detecting intumor cells obtained from a subject in need thereof the expression oractivity of a malignant cell gene signature comprising: a) one or moregenes or polypeptides selected from the group consisting of ACTB, AEN,ANP32E, ATP5A1, ATP5G2, BZW2, C17orf76-AS1, C1QBP, C20orf112, CA14,CBX5, CCT2, CCT3, CDK4, CFL1, CNRIP1, CRABP2, CS, CTPS1, DCAF13, DCT,DDX39B, DLL3, EEF1G, EIF2S3, EIF3K, EIF4A1, EIF4EBP2, FAM174B, FBL,FBLN1, FOXRED2, FTL, FUS, GABARAP, GAS5, GNB2L1, GPATCH4, GPI, GRWD1,H3F3A, H3F3AP4, HMGA1, HNRNPA1, HNRNPA1P10, HNRNPC, HSPA8, IDH2, ILF2,ISYNA1, ITM2C, KIAA0101, MAGEA4, MDH2, METAP2, MID1, MIR4461, MLLT11,MPZL1, MRPS21, NACA, NCL, NDUFS2, NOLC1, NONO, PA2G4, PABPC1, PAFAH1B3,PFDN2, PFN1, PGAM1, PIH1D1, PPA1, PPIA, PPP2R1A, PSMD4, PTMA, RAN,RBM34, RNF2, RPAIN, RPL10A, RPL11, RPL12, RPL13, RPL13A, RPL13AP5,RPL17, RPL18, RPL18A, RPL21, RPL26, RPL28, RPL29, RPL3, RPL31, RPL36A,RPL37, RPL37A, RPL39, RPL4, RPL41, RPL5, RPL6, RPL8, RPLP0, RPLP1,RPS10, RPS11, RPS12, RPS15A, RPS16, RPS17, RPS17L, RPS18, RPS19, RPS21,RPS23, RPS24, RPS26, RPS27, RPS27A, RPS3, RPS4X, RPS5, RPS6, RPS7, RPS8,RPS9, RPSA, RUVBL2, SAE1, SCD, SCNM1, SERPINF1, SET, SF3B4, SHMT2, SKP2,SLC25A3, SMS, SNAI2, SNHG6, SNRPE, SOX4, SRP14, SSR2, TIMM50, TMC6,TP53, TRPM1, TSR1, TUBA1B, TUBB, TULP4, UBA52, UQCRFS1, UQCRH, USP22,VCY1B, VDAC2, VPS72, YWHAE, ZNF286A, A2M, ACSL3, ACSL4, ADM, AEBP1, AGA,AHNAK, ANGPTL4, ANXA1, ANXA2, APLP2, APOD, APOE, ARL6IP5, ATF3, ATP1A1,ATP1B1, ATP1B3, B2M, BACE2, BBX, BCL6, CALU, CASP1, CAST, CAV1, CCND3,CD151, CD44, CD47, CD58, CD59, CD63, CD9, CDH19, CHI3L1, CLIC4, CRELD1,CRYAB, CSGALNACT1, CSPG4, CST3, CTSA, CTSB, CTSD, CTSL1, DAG1, DCBLD2,DDR1, DDX5, DPYSL2, DUSP4, DUSP6, ECM1, EEA1, EGR1, EMP1, EPHX2, ERBB3,EVA1A, EZH1, FAM3C, FBXO32, FCGR2C, FCRLA, FGFR1, FLJ43663, FOS, GAA,GADD45B, GEM, GOLGB1, GPNMB, GRN, GSN, HLA-A, HLA-B, HLA-C, HLA-E,HLA-F, HLA-H, HPCAL1, HSPA1A, HTATIP2, IFI35, IGF1R, IL1RAP, IL6ST,ITGA3, ITGA6, ITGB1, ITGB3, ITM2B, JUN, KCNN4, KLF4, KLF6, LAMP2,LEPROT, LGALS1, LGALS3, LGALS3BP, LPL, LRPAP1, MAGEC2, MFGE8, MFI2, MIA,MT1E, MT1M, MT1X, MT2A, NEAT1, NFKBIA, NFKBIZ, NNMT, NPC1, NPC2, NR4A1,NSG1, PDK4, PLP2, PRKCDBP, PRNP, PROS1, PRSS23, PSAP, PSMB9, PTRF,RNF145, RPS4Y1, S100A6, S100B, SAT1, SCARB2, SCCPDH, SDC3, SEL1L,SEMA3B, SERPINA3, SERPINE2, SGCE, SGK1, SLC20A1, SLC26A2, SLC39A14,SLC5A3, SOD1, SPRY2, SQSTM1, SRPX, STOM, SYNGR2, SYPL1, TAPBP, TAPBPL,TF, TGOLN2, TIMP1, TIMP2, TIMP3, TIPARP, TM4SF1, TMED10, TMED9, TMEM66,TMX4, TNC, TPP1, TSC22D3, TYR, UBC, UPP1, ZBTB20 and ZBTB38; or b) oneor more genes or polypeptides selected from the group consisting of AEN,ATP5A1, C20orf112, CCT2, DCAF13, DDX39B, ISYNA1, NDUFS2, NOLC1, PA2G4,PPP2R1A, RBM34, RNF2, RPL6, RPL21, SERPINF1, SF3B4, SMS, TMC6, VPS72,ANXA1, ATF3, BCL6, CD58, CD9, CTSB, DCBLD2, EMP1, HLA-F, HTATIP2,IL1RAP, ITGA6, KCNN4, KLF4, MT1E, MT1M, MT1X, MT2A, NNMT, PRKCDBP,S100A6 and TSC22D3; or c) one or more genes or polypeptides selectedfrom the group consisting of ACTB, ANP32E, CBX5, FUS, HNRNPA1, IDH2,KIAA0101, NCL, PFN1, PPIA, PTMA, RAN, RPLP0, TUBA1B, TUBB, VCY1B, A2M,APOD, BCL6, CD44, CD59, CD63, CDH19, CHI3L1, CTSA, CTSB, CTSD, FOS,GPNMB, GRN, HLA-A, HLA-B, HLA-H, ITM2B, LGALS3BP, NEAT1, PDK4, PSAP,SCARB2, SERPINA3, SLC26A2, TAPBPL, TMEM66 and TYR; or d) one or moregenes or polypeptides selected from the group consisting of MT1E, MT1M,MT1X and MT2A; or e) one or more down regulated genes selected from thegroup consisting of genes associated with coagulation, apoptosis, TNF-αsignaling via NFκb, Antigen processing and presentation, metallothioneinand IFNGR2; and/or f) one or more up regulated genes selected from thegroup consisting of genes associated with negative regulation ofangiogenesis and MYC targets.
 5. The method according to claim 4,wherein said ICR signature comprises an ICR-down signature, saidsignature comprising one or more genes selected from the groupconsisting of: a) A2M, ACSL3, ACSL4, ADM, AEBP1, AGA, AHNAK, ANGPTL4,ANXA1, ANXA2, APLP2, APOD, APOE, ARL6IP5, ATF3, ATP1A1, ATP1B1, ATP1B3,B2M, BACE2, BBX, BCL6, CALU, CASP1, CAST, CAV1, CCND3, CD151, CD44,CD47, CD58, CD59, CD63, CD9, CDH19, CHI3L1, CLIC4, CRELD1, CRYAB,CSGALNACT1, CSPG4, CST3, CTSA, CTSB, CTSD, CTSL1, DAG1, DCBLD2, DDR1,DDX5, DPYSL2, DUSP4, DUSP6, ECM1, EEA1, EGR1, EMP1, EPHX2, ERBB3, EVA1A,EZH1, FAM3C, FBXO32, FCGR2C, FCRLA, FGFR1, FLJ43663, FOS, GAA, GADD45B,GEM, GOLGB1, GPNMB, GRN, GSN, HLA-A, HLA-B, HLA-C, HLA-E, HLA-F, HLA-H,HPCAL1, HSPA1A, HTATIP2, IFI35, IGF1R, IL1RAP, IL6ST, ITGA3, ITGA6,ITGB1, ITGB3, ITM2B, JUN, KCNN4, KLF4, KLF6, LAMP2, LEPROT, LGALS1,LGALS3, LGALS3BP, LPL, LRPAP1, MAGEC2, MFGE8, MFI2, MIA, MT1E, MT1M,MT1X, MT2A, NEAT1, NFKBIA, NFKBIZ, NNMT, NPC1, NPC2, NR4A1, NSG1, PDK4,PLP2, PRKCDBP, PRNP, PROS1, PRSS23, PSAP, PSMB9, PTRF, RNF145, RPS4Y1,S100A6, S100B, SAT1, SCARB2, SCCPDH, SDC3, SEL1L, SEMA3B, SERPINA3,SERPINE2, SGCE, SGK1, SLC20A1, SLC26A2, SLC39A14, SLC5A3, SOD1, SPRY2,SQSTM1, SRPX, STOM, SYNGR2, SYPL1, TAPBP, TAPBPL, TF, TGOLN2, TIMP1,TIMP2, TIMP3, TIPARP, TM4SF1, TMED10, TMED9, TMEM66, TMX4, TNC, TPP1,TSC22D3, TYR, UBC, UPP1, ZBTB20 and ZBTB38; or b) ANXA1, ATF3, BCL6,CD58, CD9, CTSB, DCBLD2, EMP1, HLA-F, HTATIP2, IL1RAP, ITGA6, KCNN4,KLF4, MT1E, MT1M, MT1X, MT2A, NNMT, PRKCDBP, S100A6 and TSC22D3; or c)A2M, APOD, BCL6, CD44, CD59, CD63, CDH19, CHI3L1, CTSA, CTSB, CTSD, FOS,GPNMB, GRN, HLA-A, HLA-B, HLA-H, ITM2B, LGALS3BP, NEAT1, PDK4, PSAP,SCARB2, SERPINA3, SLC26A2, TAPBPL, TMEM66 and TYR, wherein said ICR-downsignature is downregulated in a tumor with a high ICR score andupregulated in a tumor with a low ICR score; and/or wherein said ICRsignature comprises an ICR-up signature, said signature comprising oneor more genes selected from the group consisting of: a) ACTB, AEN,ANP32E, ATP5A1, ATP5G2, BZW2, C17orf76-AS1, C1QBP, C20orf112, CA14,CBX5, CCT2, CCT3, CDK4, CFL1, CNRIP1, CRABP2, CS, CTPS1, DCAF13, DCT,DDX39B, DLL3, EEF1G, EIF2S3, EIF3K, EIF4A1, EIF4EBP2, FAM174B, FBL,FBLN1, FOXRED2, FTL, FUS, GABARAP, GAS5, GNB2L1, GPATCH4, GPI, GRWD1,H3F3A, H3F3AP4, HMGA1, HNRNPA1, HNRNPA1P10, HNRNPC, HSPA8, IDH2, ILF2,ISYNA1, ITM2C, KIAA0101, MAGEA4, MDH2, METAP2, MID1, MIR4461, MLLT11,MPZL1, MRPS21, NACA, NCL, NDUFS2, NOLC1, NONO, PA2G4, PABPC1, PAFAH1B3,PFDN2, PFN1, PGAM1, PIH1D1, PPA1, PPIA, PPP2R1A, PSMD4, PTMA, RAN,RBM34, RNF2, RPAIN, RPL10A, RPL11, RPL12, RPL13, RPL13A, RPL13AP5,RPL17, RPL18, RPL18A, RPL21, RPL26, RPL28, RPL29, RPL3, RPL31, RPL36A,RPL37, RPL37A, RPL39, RPL4, RPL41, RPL5, RPL6, RPL8, RPLP0, RPLP1,RPS10, RPS11, RPS12, RPS15A, RPS16, RPS17, RPS17L, RPS18, RPS19, RPS21,RPS23, RPS24, RPS26, RPS27, RPS27A, RPS3, RPS4X, RPS5, RPS6, RPS7, RPS8,RPS9, RPSA, RUVBL2, SAE1, SCD, SCNM1, SERPINF1, SET, SF3B4, SHMT2, SKP2,SLC25A3, SMS, SNAI2, SNHG6, SNRPE, SOX4, SRP14, SSR2, TIMM50, TMC6,TP53, TRPM1, TSR1, TUBA1B, TUBB, TULP4, UBA52, UQCRFS1, UQCRH, USP22,VCY1B, VDAC2, VPS72, YWHAE and ZNF286A, or b) AEN, ATP5A1, C20orf112,CCT2, DCAF13, DDX39B, ISYNA1, NDUFS2, NOLC1, PA2G4, PPP2R1A, RBM34,RNF2, RPL6, RPL21, SERPINF1, SF3B4, SMS, TMC6, VPS72, or c) ACTB,ANP32E, CBX5, FUS, HNRNPA1, IDH2, KIAA0101, NCL, PFN1, PPIA, PTMA, RAN,RPLP0, TUBA1B, TUBB and VCY1B, wherein said ICR-up signature isupregulated in a tumor with a high ICR score and downregulated in atumor with a low ICR score.
 6. (canceled)
 7. The method according toclaim 1, wherein the ICR signature is detected in cycling cells and/orexpanded cells.
 8. A method of detecting an immune cell exclusion genesignature in a tumor comprising, detecting in tumor cells obtained froma subject in need thereof the expression or activity of a malignant cellgene signature comprising: a) one or more genes or polypeptides selectedfrom the group consisting of ACAT1, ACP5, ACTG1, ADSL, AK2, APP, ASAP1,ATP5D, BANCR, BCAN, BZW2, C17orf76-AS1, C1QBP, C6orf48, CA14, CCT3,CCT6A, CEP170, CHP1, CTPS1, CYC1, DAP3, DCT, DDX21, EDNRB, EEF1D, EEF1G,EEF2, EIF1AX, EIF2S3, EIF3E, EIF3K, EIF3L, EIF4A1, ESRP1, FAM178B,FAM92A1, FTL, GAS5, GNB2L1, GPI, GSTO1, IFI16, ILF2, IMPDH2, LHFPL3-AS1,LOC100190986, LYPLA1, MARCKS, MDH2, MRPL37, MRPS12, MYC, NCL, NF2, NID1,NOLC1, NPM1, NUCKS1, OAT, PABPC1, PAICS, PLTP, PSAT1, PYCARD, RASA3,RPL10, RPL10A, RPL11, RPL12, RPL13, RPL13A, RPL13AP5, RPL14, RPL17,RPL18, RPL18A, RPL28, RPL29, RPL3, RPL30, RPL35, RPL37A, RPL39, RPL4,RPL5, RPL6, RPL7, RPL7A, RPL8, RPLP0, RPLP1, RPS10, RPS11, RPS15,RPS15A, RPS16, RPS17, RPS17L, RPS18, RPS19, RPS2, RPS24, RPS27, RPS3,RPS3A, RPS4X, RPS5, RPS7, RPS8, RPS9, RPSA, RSL1D1, SCD, SERBP1,SERPINF1, SLC19A1, SLC25A5, SLC25A6, SNAI2, SNHG16, SNHG6, SORD, SOX4,TIMM13, TIMM50, TOP1MT, TRAP1, TUBB4A, TXLNA, TYRP1, UCK2, UQCRFS1,ZFAS1, A2M, AGA, AHNAK, ANXA1, APLP2, APOC2, ARF5, ATP1A1, ATP1B1,ATRAID, B2M, C10orf54, C4A, CBLB, CCND3, CD151, CD47, CD58, CD59, CDH19,CHN1, CLU, CPVL, CST3, CTSB, CTSD, CTSL1, DDR1, DPYSL2, DSCR8, DUSP6,DYNLRB1, EMP1, EZR, FAM3C, FGFR1, FYB, GAA, GATSL3, GRN, GSN, HCP5,HLA-B, HLA-C, HLA-F, HLA-H, HSPA1A, HSPA1B, ID2, IFI27L2, ISCU, ITGA3,ITGA7, ITGB3, KCNN4, KRT10, LOC100506190, LTBP3, LYRM9, MAEL, MAP1B,MATN2, MFGE8, MFI2, MIA, MRPS6, MT2A, NDRG1, NFKBIA, NPC1, OCIAD2,PAGES, PERP, PKM, RDH5, S100A13, S100A6, SERPINA1, SERPINA3, SERPINE2,SGCE, SLC26A2, SLC5A3, SNX9, SPON2, THBD, TIMP1, TM4SF1, TMBIM6, TNFSF4,TPP1, TRIML2, TSC22D3, TSPYL2, TXNIP, UBC, XAGE1A, XAGE1B, XAGE1C,XAGE1D and XAGE1E; or b) one or more genes or polypeptides selected fromthe group consisting of ACTG1, ADSL, C17orf76-AS1, C1QBP, CTPS1, EIF2S3,EIF3E, ILF2, NCL, NF2, NOLC1, PABPC1, PAICS, RPL10A, RPL18, RPL6, RPS24,RSL1D1, SERPINF1, SOX4, AHNAK, ANXA1, CCND3, CD151, CD47, CD58, CST3,CTSB, CTSD, EMP1, FGFR1, HLA-C, HLA-F, ITGB3, KCNN4, MIA, MT2A, S100A6,SLC5A3, TIMP1 and TSC22D3; or c) one or more genes or polypeptidesselected from the group consisting of C17orf76-AS1, C1QBP, CTPS1,EIF2S3, ILF2, NCL, NOLC1, PABPC1, RPL10A, RPL18, RPL6, RPS24, SERPINF1,SOX4, AHNAK, ANXA1, CCND3, CD151, CD47, CD58, CST3, CTSB, CTSD, EMP1,FGFR1, HLA-C, HLA-F, ITGB3, KCNN4, MIA, MT2A, S100A6, SLC5A3, TIMP1 andTSC22D3.
 9. The method according to claim 8, wherein said exclusionsignature comprises an exclusion-down signature, said signaturecomprising one or more genes selected from the group consisting of: a)A2M, AGA, AHNAK, ANXA1, APLP2, APOC2, ARF5, ATP1A1, ATP1B1, ATRAID, B2M,C10orf54, C4A, CBLB, CCND3, CD151, CD47, CD58, CD59, CDH19, CHN1, CLU,CPVL, CST3, CTSB, CTSD, CTSL1, DDR1, DPYSL2, DSCR8, DUSP6, DYNLRB1,EMP1, EZR, FAM3C, FGFR1, FYB, GAA, GATSL3, GRN, GSN, HCP5, HLA-B, HLA-C,HLA-F, HLA-H, HSPA1A, HSPA1B, ID2, IFI27L2, ISCU, ITGA3, ITGA7, ITGB3,KCNN4, KRT10, LOC100506190, LTBP3, LYRM9, MAEL, MAP1B, MATN2, MFGE8,MFI2, MIA, MRPS6, MT2A, NDRG1, NFKBIA, NPC1, OCIAD2, PAGES, PERP, PKM,RDH5, S100A13, S100A6, SERPINA1, SERPINA3, SERPINE2, SGCE, SLC26A2,SLC5A3, SNX9, SPON2, THBD, TIMP1, TM4SF1, TMBIM6, TNFSF4, TPP1, TRIML2,TSC22D3, TSPYL2, TXNIP, UBC, XAGE1A, XAGE1B, XAGE1C, XAGE1D and XAGE1E);or b) AHNAK, ANXA1, CCND3, CD151, CD47, CD58, CST3, CTSB, CTSD, EMP1,FGFR1, HLA-C, HLA-F, ITGB3, KCNN4, MIA, MT2A, S100A6, SLC5A3, TIMP1 andTSC22D3, wherein said exclusion-down signature is downregulated in atumor with T cell exclusion and is upregulated in a tumor with T cellinfiltration, and/or wherein said exclusion signature comprises anexclusion-up signature, said signature comprising one or more genesselected from the group consisting of: a) ACAT1, ACP5, ACTG1, ADSL, AK2,APP, ASAP1, ATP5D, BANCR, BCAN, BZW2, C17orf76-AS1, C1QBP, C6orf48,CA14, CCT3, CCT6A, CEP170, CHP1, CTPS1, CYC1, DAP3, DCT, DDX21, EDNRB,EEF1D, EEF1G, EEF2, EIF1AX, EIF2S3, EIF3E, EIF3K, EIF3L, EIF4A1, ESRP1,FAM178B, FAM92A1, FTL, GAS5, GNB2L1, GPI, GSTO1, IFI16, ILF2, IMPDH2,LHFPL3-AS1, LOC100190986, LYPLA1, MARCKS, MDH2, MRPL37, MRPS12, MYC,NCL, NF2, NID1, NOLC1, NPM1, NUCKS1, OAT, PABPC1, PAICS, PLTP, PSAT1,PYCARD, RASA3, RPL10, RPL10A, RPL11, RPL12, RPL13, RPL13A, RPL13AP5,RPL14, RPL17, RPL18, RPL18A, RPL28, RPL29, RPL3, RPL30, RPL35, RPL37A,RPL39, RPL4, RPL5, RPL6, RPL7, RPL7A, RPL8, RPLP0, RPLP1, RPS10, RPS11,RPS15, RPS15A, RPS16, RPS17, RPS17L, RPS18, RPS19, RPS2, RPS24, RPS27,RPS3, RPS3A, RPS4X, RPS5, RPS7, RPS8, RPS9, RPSA, RSL1D1, SCD, SERBP1,SERPINF1, SLC19A1, SLC25A5, SLC25A6, SNAI2, SNHG16, SNHG6, SORD, SOX4,TIMM13, TIMM50, TOP1MT, TRAP1, TUBB4A, TXLNA, TYRP1, UCK2, UQCRFS1 andZFAS1, or b) ACTG1, ADSL, C17orf76-AS1, C1QBP, CTPS1, EIF2S3, EIF3E,ILF2, NCL, NF2, NOLC1, PABPC1, PAICS, RPL10A, RPL18, RPL6, RPS24,RSL1D1, SERPINF1 and SOX4, or c) C17orf76-AS1, C1QBP, CTPS1, EIF2 S3,ILF2, NCL, NOLC1, PABPC1, RPL10A, RPL18, RPL6, RPS24, SERPINF1 and SOX4,wherein said exclusion-up signature is upregulated in a tumor with Tcell exclusion and is downregulated in a tumor with T cell infiltration.10. (canceled)
 11. The method according to claim 1, further comprisingdetecting tumor infiltrating lymphocytes (TIL).
 12. The method accordingto claim 1, wherein the gene signature is detected in a bulk tumorsample, whereby the gene signature is detected by deconvolution of bulkexpression data such that gene expression is assigned to malignant cellsand non-malignant cells in said tumor sample.
 13. The method accordingto claim 2, wherein detecting the gene signature comprises detectingdownregulation of the down signature and/or upregulation of the upsignature, and wherein not detecting the gene signature comprisesdetecting upregulation of the down signature and/or downregulation ofthe up signature.
 14. The method according to claim 13, whereindetecting the signature and/or TILs indicates lower progression freesurvival and/or resistance to checkpoint blockade therapy, and whereinnot detecting the signature and/or TILs indicates higher progressionfree survival and/or sensitivity to checkpoint blockade therapy.
 15. Themethod according to claim 13, wherein detecting the gene signatureindicates a 10-year survival rate less than 40% and wherein notdetecting the signature indicates a 10-year survival rate greater than60%.
 16. The method according to claim 1, wherein detecting an ICRsignature in a tumor further comprises detecting in tumor infiltratinglymphocytes (TIL) obtained from the subject in need thereof theexpression or activity of a CD8 T cell gene signature, said signaturecomprising one or more genes or polypeptides selected from the groupconsisting of CEP19, EXO5, FAM153C, FCRL6, GBP2, GBP5, HSPA1B, IER2,IRF1, KLRK1, LDHA, LOC100506083, MBOAT1, SEMA4D, SIRT3, SPDYE2, SPDYE2L,STAT1, STOM, UBE2Q2P3, ACP5, AKNA, BTN3A2, CCDC141, CD27, CDC42SE1,DDIT4, FAU, FKBP5, GPR56, HAVCR2, HLA-B, HLA-C, HLA-F, IL6ST, ITGA4,KIAA1551, KLF12, MIR155HG, MTA2, MTRNR2L1, MTRNR2L3, PIK3IP1, RPL26,RPL27, RPL27A, RPL35A, RPS11, RPS16, RPS20, RPS26, SPOCK2, SYTL3, TOB1,TPT1, TTN, TXNIP, WNK1 and ZFP36L2; and/or wherein detecting an ICRsignature in a tumor further comprises detecting in macrophages obtainedfrom the subject in need thereof the expression or activity of amacrophage gene signature, said signature comprising one or more genesor polypeptides selected from the group consisting of APOL1, CD274,CSTB, DCN, HLA-DPB2, HLA-DQA1, HLA-G, HSPA8, HSPB1, IL18BP, TMEM176A,UBD, A2M, ADAP2, ADORA3, ARL4C, ASPH, BCAT1, C11orf31, C3, C3AR1,C6orf62, CAPN2, CD200R1, CD28, CD9, CD99, COMT, CREM, CRTAP, CYFIP1,DDOST, DHRS3, EGFL7, EIF1AY, ETS2, FCGR2A, FOLR2, GATM, GBP3, GNG2,GSTT1, GYPC, HIST1H1E, HPGDS, IFI44L, IGFBP4, ITGA4, KCTD12, LGMN,LOC441081, LTC4S, LYVE1, MERTK, METTL7B, MS4A4A, MS4A7, MTSS1, NLRP3,OLFML3, PLA2G15, PLXDC2, PMP22, POR, PRDX2, PTGS1, RNASE1, ROCK1,RPS4Y1, S100A9, SCAMP2, SEPP1, SESN1, SLC18B1, SLC39A1, SLC40A1, SLC7A8,SORL1, SPP1, STAB1, TMEM106C, TMEM86A, TMEM9, TNFRSF1B, TNFRSF21,TPD52L2, ULK3 and ZFP36L2.
 17. (canceled)
 18. A method of stratifyingcancer patients into a high survival group and a low survival groupcomprising detecting the expression or activity of an ICR and/orexclusion signature in a tumor according to claim 1, wherein if thesignature is detected the patient is in the low survival group and ifthe signature is not detected the patient is in the high survival group,preferably, wherein patients in the high survival group areimmunotherapy responders and patients in the low survival group areimmunotherapy non-responders.
 19. (canceled)
 20. A method of treating acancer in a subject in need thereof comprising detecting the expressionor activity of an ICR and/or exclusion signature according to claim 1 ina tumor obtained from the subject and administering a treatment, whereinif an ICR and/or exclusion signature is detected the treatment comprisesadministering an agent capable of reducing expression or activity ofsaid signature, and wherein if an ICR and/or exclusion signature is notdetected the treatment comprises administering an immunotherapy.
 21. Themethod according to claim 20, wherein the agent capable of reducingexpression or activity of said signature comprises a CDK4/6 inhibitor, adrug selected from Table 3, a cell cycle inhibitor, a PKC activator, aninhibitor of the NFκB pathway, an IGF1R inhibitor, or Reserpine; orwherein the agent capable of reducing expression or activity of saidsignature comprises an agent capable of modulating expression oractivity of a gene selected from the group consisting of MAZ, NFKBIZ,MYC, ANXA1, SOX4, MT2A, PTP4A3, CD59, DLL3, SERPINE2, SERPINF1, PERP,EGR1, SERPINA3, SEMA3B, SMARCA4, IFNGR2, B2M, and PDL1, or wherein theagent capable of reducing expression or activity of said signaturecomprises an agent capable of targeting or binding to one or moreup-regulated secreted or cell surface exposed ICR and/or exclusionsignature genes or polypeptides.
 22. (canceled)
 23. (canceled)
 24. Themethod according to claim 20, wherein the method further comprisesdetecting the expression or activity of the ICR and/or exclusionsignature in a tumor obtained from the subject after the treatment andadministering an immunotherapy if said signature is reduced or below areference level; or wherein the method further comprises detecting theexpression or activity of the ICR and/or exclusion signature in a tumorobtained from the subject before the treatment and administering animmunotherapy if said signature is not detected or below a referencelevel; or wherein the method further comprises administering animmunotherapy to the subject administered an agent capable of reducingthe expression or activity of said signature.
 25. The method accordingto claim 24, wherein the agent capable of reducing expression oractivity of said signature is a CDK4/6 inhibitor.
 26. (canceled) 27.(canceled)
 28. The method according to claim 20, wherein theimmunotherapy comprises a check point inhibitor or adoptive celltransfer (ACT), preferably, wherein adoptive cell transfer comprises aCAR T cell or activated autologous T cells and wherein the checkpointinhibitor comprises anti-CTLA4, anti-PD-L1 and/or anti-PD1 therapy. 29.(canceled)
 30. (canceled)
 31. A method of treating a cancer in a subjectin need thereof comprising detecting the expression or activity of anICR and/or exclusion signature according to claim 1 in a tumor obtainedfrom the subject, wherein if an ICR and/or exclusion signature isdetected the treatment comprises administering an agent capable ofmodulating expression or activity of one or more genes or polypeptidesin a network of genes disrupted by perturbation of a gene selected fromthe group consisting of MAZ, NFKBIZ, MYC, ANXA1, SOX4, MT2A, PTP4A3,CD59, DLL3, SERPINE2, SERPINF1, PERP, EGR1, SERPINA3, SEMA3B, SMARCA4,IFNGR2, B2M, and PDL1.
 32. A method of treating a cancer in a subject inneed thereof comprising administering to the subject a therapeuticallyeffective amount of an agent: a) capable of modulating the expression oractivity of one or more ICR and/or exclusion signature genes orpolypeptides according to claim 1; or b) capable of targeting or bindingto one or more cell surface exposed ICR and/or exclusion signature genesor polypeptides, wherein the gene or polypeptide is up-regulated in theICR and/or exclusion signature; or c) capable of targeting or binding toone or more receptors or ligands specific for a cell surface exposed ICRand/or exclusion signature gene or polypeptide, wherein the gene orpolypeptide is up-regulated in the ICR and/or exclusion signature; or d)comprising a secreted ICR and/or exclusion signature gene orpolypeptide, wherein the gene or polypeptide is down-regulated in theICR and/or exclusion signature; or e) capable of targeting or binding toone or more secreted ICR and/or exclusion signature genes orpolypeptides, wherein the genes or polypeptides are up-regulated in theICR and/or exclusion signature; or f) capable of targeting or binding toone or more receptors specific for a secreted ICR and/or exclusionsignature gene or polypeptide, wherein the secreted gene or polypeptideis up-regulated in the ICR and/or exclusion signature; or g) comprisinga CDK4/6 inhibitor, a drug selected from Table 3, a cell cycleinhibitor, a PKC activator, an inhibitor of the NFκB pathway, an IGF1Rinhibitor, or Reserpine.
 33. The method according to claim 32, whereinsaid agent comprises a therapeutic antibody, antibody fragment,antibody-like protein scaffold, aptamer, protein, CRISPR system or smallmolecule; or wherein said agent capable of targeting or binding to oneor more cell surface exposed ICR and/or exclusion signature polypeptidesor one or more receptors specific for a secreted ICR and/or exclusionsignature gene or polypeptide comprises a CAR T cell capable oftargeting or binding to one or more cell surface exposed ICR and/orexclusion signature genes or polypeptides or one or more receptorsspecific for a secreted ICR and/or exclusion signature gene orpolypeptide; or wherein said agent capable of modulating the expressionor activity of one or more ICR and/or exclusion signature genes orpolypeptides comprises a CDK4/6 inhibitor, preferably, wherein theCDK4/6 inhibitor comprises abemaciclib.
 34. (canceled)
 35. (canceled)36. (canceled)
 37. The method according to claim 33, wherein the methodfurther comprises administering an immunotherapy to the subject,preferably, wherein the immunotherapy comprises a check point inhibitor,more preferably, wherein the checkpoint inhibitor comprises anti-CTLA4,anti-PD-L1 and/or anti-PD1 therapy.
 38. (canceled)
 39. (canceled)
 40. Amethod of monitoring a cancer in a subject in need thereof comprisingdetecting the expression or activity of an ICR and/or exclusion genesignature according to claim 1 in tumor samples obtained from thesubject for at least two time points, preferably, wherein at least onesample is obtained before treatment, and/or wherein at least one sampleis obtained after treatment.
 41. (canceled)
 42. (canceled)
 43. Themethod of claim 1, wherein the cancer is melanoma or breast cancer. 44.The method of claim 1, wherein the ICR and/or exclusion signature isexpressed in response to administration of an immunotherapy.
 45. Themethod of claim 4, wherein the subject in need thereof has been treatedwith an immunotherapy.
 46. A kit comprising reagents to detect at leastone ICR and/or exclusion signature gene or polypeptide according toclaim 1, preferably, wherein the kit comprises at least one antibody,antibody fragment, or aptamer; or wherein the kit comprises primersand/or probes for quantitative RT-PCR or fluorescently bar-codedoligonucleotide probes for hybridization to RNA.
 47. (canceled) 48.(canceled)